Probability and statistics Books

Book SynopsisThis is the sixth edition of a popular textbook on multivariate analysis. Well-regarded for its practical and accessible approach, with excellent examples and good guidance on computing, the book is particularly popular for teaching outside statistics, i.e. in epidemiology, social science, business, etc. The sixth edition has been updated with a new chapter on data visualization, a distinction made between exploratory and confirmatory analyses and a new section on generalized estimating equations and many new updates throughout. This new edition will enable the book to continue as one of the leading textbooks in the area, particularly for non-statisticians. Key Features: Provides a comprehensive, practical and accessible introduction to multivariate analysis. Keeps mathematical details to a minimum, so particularly geared toward a non-statistical audience. Includes lots of detailed worked examples, guidance on computiTrade Review"This book is an excellent resource for students and researchers of all levels. I have used earlier editions repeatedly in data-analysis courses for advanced undergraduates and graduate students in applied fields. The level of mathematical presentation is well matched to such settings. Not only are there excellent examples from biostatistics and public health, but there are also some very good business financial examples. The new chapter on Data Visualization in the new, sixth edition will be especially useful. Overall, the book is exceptionally well written and readable."- Stanley Sclove, University of Illinois at Chicago "Editions of Practical Multivariate Analysis have been the mainstay of my graduate-level service course in applied data-analysis since 1985. It remains an extraordinary book -- packed with excellent examples, clear explanation and fine advice -- and has my highest possible recommendation. Among many reasons it remains so extraordinary, are three signaled directly in its title: it is practical rather than theoretical, analytic rather than technical, and it embodies a broader-than-usual conception of utilitarian multivariate methods. Practical Multivariate Analysis connects readily to its audience’s reality. It uses concrete research questions and real data to motivate its content, illustrated by exemplary analyses using R, SAS, SPSS and STATA. It models how complex findings can be made comprehensible to a broader community. It reaches beyond the typical spectrum of multivariate methods. It begins sensibly, discussing how multivariate data can be explored and displayed before complex analysis. Then come chapters on useful extensions to multiple regression analysis. While not usually considered “multivariate,” these latter methods connect an incoming audience to earlier acquired skills and extend them. Then follow the core chapters on “standard” multivariate methods, including canonical correlation, discriminant, principal-components, factor and cluster analyses. All are clearly presented, and then extended by excellent chapters on logistic regression, survival and log-linear analyses, and multilevel modeling, techniques that have proven useful and ubiquitous throughout social-science research.In my view, Practical Multivariate Analysis is an excellent roadmap for conducting such analyses, and a fine model for ensuring that their complex findings can be communicated successfully to others."- John B. Willett, Charles William Eliot Research Professor, Harvard University Graduate School of Education "The Practical Multivariate Analysis is a fun statistical modeling book to read. I enjoyed the rich insights the book has provided, which can only be accumulated through years of experience with the complexity in real data. It covers a large collection of statistical methods and models with a clear focus on application. Always discussing a model or method along with data examples, the book helps readers focus on important perspectives in applying the model, from choice of appropriate methods to interpretation of the results, while it still manages to maintain thetechnique details at a minimal level. Readers with different backgrounds can all benefit from this book. It is valuable for researchers who are interested in analyzing their data with classical statistical models and interpreting the results. It is a good reading for new graduates in statistics who have not had a lot of experience with real data as the book provides many importance guidance in handling real data as well as watch-out advices. It can be used by applied data scientists and serve as a resourceful reference book for experienced consultants."- Xia Wang, University of Cincinnati "The monograph belongs to the series Texts in Statistical Science and presents the sixth upgraded edition of the popular manual. It was first issued in 1984, and from that time won recognition as one of the best textbooks on the applied statistical modeling and analysis...Most of chapters of the first part of the textbook contain such subsections as “Introduction” or “Definition,” “Discussion” or “Examples,” “Summary” and “Problems”...This structure makes the book very reader-friendly written, helping to students and researchers in various fields to understand what for a statistical tool can serve, how to apply it, and to interpret computer outputs. There is not much of mathematical and statistical derivation, neither modern statistical techniques, but plenty of examples oriented to the easy “know-how” practical implementations of the classical multivariate methods."- Stan Lipovetsky, Technometrics, Vol 62"The authors wrote the sixth edition of this book for biomedical scientists, behavioural scientists, and academic researchers, who wish to perform and understand the results of multivariate statistical analyses. The book also describes when to ask for help from a statistical expert on multivariate analysis...The sixth edition has been updated with, in particular, a new chapter on data visualization, a distinction made between exploratory and confirmatory analyses, and a new section on generalized estimating equations. This new edition will enable the book to continue as one of the leading textbooks in the area, particularly for non-statisticians, since it provides a comprehensive, practical, and accessible introduction to multivariate analysis whilst keeping mathematical details to a minimum...The book is an excellent roadmap for multivariate analysis and a fine model for ensuring that complex findings can be successfully communicated in a paper."- Luca Bertolaccini, ISCB News, July 2020 "This book is an excellent resource for students and researchers of all levels. I have used earlier editions repeatedly in data-analysis courses for advanced undergraduates and graduate students in applied fields. The level of mathematical presentation is well matched to such settings. Not only are there excellent examples from biostatistics and public health, but there are also some very good business financial examples. The new chapter on Data Visualization in the new, sixth edition will be especially useful. Overall, the book is exceptionally well written and readable."- Stanley Sclove, University of Illinois at Chicago "Editions of Practical Multivariate Analysis have been the mainstay of my graduate-level service course in applied data-analysis since 1985. It remains an extraordinary book -- packed with excellent examples, clear explanation and fine advice -- and has my highest possible recommendation. Among many reasons it remains so extraordinary, are three signaled directly in its title: it is practical rather than theoretical, analytic rather than technical, and it embodies a broader-than-usual conception of utilitarian multivariate methods. Practical Multivariate Analysis connects readily to its audience’s reality. It uses concrete research questions and real data to motivate its content, illustrated by exemplary analyses using R, SAS, SPSS and STATA. It models how complex findings can be made comprehensible to a broader community. It reaches beyond the typical spectrum of multivariate methods. It begins sensibly, discussing how multivariate data can be explored and displayed before complex analysis. Then come chapters on useful extensions to multiple regression analysis. While not usually considered “multivariate,” these latter methods connect an incoming audience to earlier acquired skills and extend them. Then follow the core chapters on “standard” multivariate methods, including canonical correlation, discriminant, principal-components, factor and cluster analyses. All are clearly presented, and then extended by excellent chapters on logistic regression, survival and log-linear analyses, and multilevel modeling, techniques that have proven useful and ubiquitous throughout social-science research.In my view, Practical Multivariate Analysis is an excellent roadmap for conducting such analyses, and a fine model for ensuring that their complex findings can be communicated successfully to others."- John B. Willett, Charles William Eliot Research Professor, Harvard University Graduate School of Education "The Practical Multivariate Analysis is a fun statistical modeling book to read. I enjoyed the richinsights the book has provided, which can only be accumulated through years of experience withthe complexity in real data. It covers a large collection of statistical methods and models with aclear focus on application. Always discussing a model or method along with data examples, thebook helps readers focus on important perspectives in applying the model, from choice ofappropriate methods to interpretation of the results, while it still manages to maintain thetechnique details at a minimal level.Readers with different backgrounds can all benefit from this book. It is valuable for researcherswho are interested in analyzing their data with classical statistical models and interpreting theresults. It is a good reading for new graduates in statistics who have not had a lot of experiencewith real data as the book provides many importance guidance in handling real data as well aswatch-out advices. It can be used by applied data scientists and serve as a resourceful referencebook for experienced consultants."- Xia Wang, University of Cincinnati "The monograph belongs to the series Texts in Statistical Science and presents the sixth upgraded edition of the popular manual. It was first issued in 1984, and from that time won recognition as one of the best textbooks on the applied statistical modeling and analysis...Most of chapters of the first part of the textbook contain such subsections as “Introduction” or “Definition,” “Discussion” or “Examples,” “Summary” and “Problems”...This structure makes the book very reader-friendly written, helping to students and researchers in various fields to understand what for a statistical tool can serve, how to apply it, and to interpret computer outputs. There is not much of mathematical and statistical derivation, neither modern statistical techniques, but plenty of examples oriented to the easy “know-how” practical implementations of the classical multivariate methods."- Stan Lipovetsky, Technometrics, Vol 62 Table of ContentsPart I: Preparation for Analysis. What is Multivariate Analysis? Characterizing Data for Analysis. Preparing for Data Analysis. Data Visualization. Data Screening and Transformations. Data Visualization. Selecting Appropriate Analyses. Part II: Regression Analysis. Simple Regression and Correlation. Multiple Regression and Correlation. Variable Selection in Regression. Special Regression Topics. Discriminant analysis. Logistic Regression. Regression Analysis with Survival Data. Principal Components Analysis. Factor Analysis. Cluster Analysis. Log-Linear Analysis. Correlated Outcomes Regression.

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Book SynopsisA leading data visualisation expert explores the negativeand positive-influences that charts have on our perception of truth.Trade Review"[Alberto Cairo's] book reminds readers not to infer too much from a chart, especially when it shows them what they already wanted to see. Mr Cairo has sent a copy to the White House." -- The Economist

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Book SynopsisStatistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications.The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, Trade Review"This book delivers a very comprehensive summary of the development of statistical foundations of data science. The authors no doubt are doing frontier research and have made several crucial contributions to the field. Therefore, the book offers a very good account of the most cutting-edge development. The book is suitable for both master and Ph.D. students in statistics, and also for researchers in both applied and theoretical data science. Researchers can take this book as an index of topics, as it summarizes in brief many significant research articles in an accessible way. Each chapter can be read independently by experienced researchers. It provides a nice cover of key concepts in those topics and researchers can benefit from reading the specific chapters and paragraphs to get a big picture rather than diving into many technical articles. There are altogether 14 chapters. It can serve as a textbook for two semesters. The book also provides handy codes and data sets, which is a great treasure for practitioners."~Journal of Time Series Analysis"This text—collaboratively authored by renowned statisticians Fan (Princeton Univ.), Li (Pennsylvania State Univ.), Zhang (Rutgers Univ.), and Zhou (Univ. of Minnesota)—laboriously compiles and explains theoretical and methodological achievements in data science and big data analytics. Amid today's flood of coding-based cookbooks for data science, this book is a rare monograph addressing recent advances in mathematical and statistical principles and the methods behind regularized regression, analysis of high-dimensional data, and machine learning. The pinnacle achievement of the book is its comprehensive exploration of sparsity for model selection in statistical regression, considering models such as generalized linear regression, penalized least squares, quantile and robust regression, and survival regression. The authors discuss sparsity not only in terms of various types of penalties but also as an important feature of numerical optimization algorithms, now used in manifold applications including deep learning. The text extensively probes contemporary high-dimensional data modeling methods such as feature screening, covariate regularization, graphical modeling, and principal component and factor analysis. The authors conclude by introducing contemporary statistical machine learning, spanning a range of topics in supervised and unsupervised learning techniques and deep learning. This book is a must-have bookshelf item for those with a thirst for learning about the theoretical rigor of data science."~Choice Review, S-T. Kim, North Carolina A&T State University, August 2021Table of Contents1. Introduction. 2. Multiple and Nonparametric Regression. 3. Introduction to Penalized Least-Squares. 4. Penalized Least Squares: Properties. 5. Generalized Linear Models and Penalized Likelihood. 6. Penalized M-estimators. 7. High Dimensional Inference 8. Feature Screening. 9. Covariance Regularization and Graphical Models. 10. Covariance Learning and Factor Models. 11. Applications of Factor Models and PCA. 12. Supervised Learning. 13. Unsupervised Learning. 14. An Introduction to Deep Learning.

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Book SynopsisRisk Analysis in Engineering and Economics is required reading for decision making under conditions of uncertainty. The author describes the fundamental concepts, techniques, and applications of the subject in a style tailored to meet the needs of students and practitioners of engineering, science, economics, and finance. Drawing on his extensive experience in uncertainty and risk modeling and analysis, the author covers everything from basic theory and key computational algorithms to data needs, sources, and collection. He emphasizes practical use of the methods presented and carefully examines the limitations, advantages, and disadvantages of each to help readers translate the discussed techniques into real-world solutions.This Second Edition: Introduces the topic of risk finance Incorporates homeland security applications throughout Offers additional material on predictive risk management Includes a wealth of neTrade ReviewPraise for the First Edition "This book ambitiously tackles risk analysis from the ground up. … an excellent reference for individuals studying in a variety of risk-related disciplines. … enlightening [examples]…attractive to students approaching risk studies from a variety of substantive backgrounds. … Advanced students are likely to find Ayyub's treatments…exceedingly valuable."—Journal of the American Statistical Association, June 2004, Vol. 99, No. 466 "… a good textbook and reference. The mix of engineering and economics is well balanced. … Valuable for students after college and useful for professional engineers."—Technometrics, August 2004, Vol. 46, No. 3 Praise for the Second Edition "… one of a kind in the market. … I am not aware of books that cover the subject in as comprehensive a manner."—Professor Nii O. Attoh-Okine, University of Delaware, Newark, USA Table of ContentsIntroduction. Risk Analysis Methods. System Definition and Structure. Reliability Assessment. Failure Consequences and Severity. Engineering Economics and Finance. Risk Control Methods. Data for Risk Studies. Fundamentals of Probability and Statistics. Failure Data. References and Bibliography. Index.

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Book SynopsisNew Tools to Solve Your Option Pricing ProblemsFor nonlinear PDEs encountered in quantitative finance, advanced probabilistic methods are needed to address dimensionality issues. Written by two leaders in quantitative researchincluding Risk magazine's 2013 Quant of the YearNonlinear Option Pricing compares various numerical methods for solving high-dimensional nonlinear problems arising in option pricing. Designed for practitioners, it is the first authored book to discuss nonlinear Black-Scholes PDEs and compare the efficiency of many different methods. Real-World Solutions for Quantitative Analysts The book helps quants develop both their analytical and numerical expertise. It focuses on general mathematical tools rather than specific financial questions so that readers can easily use the tools to solve their own nonlinear problems. The authors build intuition through numerous real-world examples of numTrade Review"... provides a wide overview of the advanced modern techniques applied in financial modeling. It gives an optimal combination of analytical and numerical tools in quantitative finance. It could provide guidance on the development of nonlinear methods of option pricing for practitioners as well as for analysts."—Nikita Y. Ratanov, from Mathematical Reviews Clippings, January 2015"… anyone with interest in quantitative finance and partial differential equations/continuous time stochastic analysis will not only greatly enjoy this book, but he or she will find both many numerical ideas of real practical interest as well as material for academic research, perhaps for years to come."—Peter Friz, The Bachelier Finance Society"This textbook provides a comprehensive treatment of numerical methods for nonlinear option pricing problems."—Zentralblatt MATH 1285"It is the only book of its kind. … The contribution of this book is threefold: (a) a practical, intuitive, and self-contained derivation of various of the latest derivative pricing models driven by diffusion processes; (b) an exposition of various advanced Monte Carlo simulation schemes for solving challenging nonlinear problems arising in financial engineering; (c) a clear and accessible survey of the theory of nonlinear PDEs. The authors have done a brilliant job providing just the right amount of rigorous theory required to understand the advanced methodologies they present. … Julien Guyon and Pierre Henry-Labordère, as befitting their reputations as star quants, have done an excellent job presenting the latest theory of nonlinear PDEs and their applications to finance. Much of the material in the book consists of the authors’ own original results. I highly recommend this book to seasoned mathematicians and experienced quants in the industry … Mathematicians will be able to see how practitioners argue heuristically to arrive at solutions of the toughest problems in financial engineering; practitioners of quantitative finance will find the book perfectly balanced between mathematical theory, financial modelling, and schemes for numerical implementation."—Quantitative Finance, 2014"Ever since Black and Scholes solved their eponymous linear PDE in 1969, the complexity of problems plaguing financial practitioners has exploded (non-linearly!). How fitting it is that nonlinear PDEs are now routinely used to extend the original framework. Written by two leading quants at two leading financial houses, this book is a tour de force on the use of nonlinear PDEs in financial valuation."—Peter Carr, PhD, Global Head of Market Modeling, Morgan Stanley, New York, and Executive Director of Masters in Mathematical Finance, Courant Institute of Mathematical Sciences, New York University "Finance used to be simple; you could go a long way with just linearity and positivity but this is not the case anymore. This superb book gives a wide array of modern methods for modern problems."—Bruno Dupire, Head of Quantitative Research, Bloomberg L.P."In this unique and impressive book, the authors apply sophisticated modern tools of pure and applied mathematics, such as BSDEs and particle methods, to solve challenging nonlinear problems of real practical interest, such as the valuation of guaranteed equity-linked annuity contracts and the calibration of local stochastic volatility models. Not only that, but sketches of proofs and implementation details are included. No serious student of mathematical finance, whether practitioner or academic, can afford to be without it."—Jim Gatheral, Presidential Professor, Baruch College, CUNY, and author of The Volatility Surface"Guyon and Henry-Labordère have produced an impressive textbook, which covers options and derivatives pricing from the point of view of nonlinear PDEs. This book is a comprehensive survey of nonlinear techniques, ranging from American options, uncertain volatility, and uncertain correlation models. It is aimed at graduate students or quantitative analysts with a strong mathematical background. They will find the book reasonably self-contained, i.e., discussing both the mathematical theory and the applications, in a very balanced approach. A must-read for the serious quantitative analyst."—Marco Avellaneda, Courant Institute of Mathematical Sciences, New York UniversityTable of ContentsSome Excursions in Option Pricing. Nonlinear PDEs: A Bit of Theory. Examples of Nonlinear Problems in Finance. Early Exercise Problems. Backward Stochastic Differential Equations. The Uncertain Lapse and Mortality Model. The Uncertain Volatility Model. McKean Nonlinear Stochastic Differential Equations. Calibration of Local Stochastic Volatility Models to Market Smiles. Calibration of Local Correlation Models to Market Smiles. Marked Branching Diffusions. References. Index.

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Book SynopsisLike the best-selling first two editions, A Handbook of Statistical Analyses using R, Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis.New to the Third Edition Three new chapters on quantile regression, missing values, and Bayesian inference Extra material in the logistic regression chapter that describes a regression model for ordered categorical response variables Additional exercises More detailed explanations of R code New section in each chapter summarizing the results of the analyses Updated version of the HSAUR package (HSAUR3), which includes some slides that can be used in introductory statistics courses Whether you're a data analyst, scientist, or student, this handTrade Review“I truly appreciate how grounded in practicality this book is—and the way its chapters are structured really underlines this. Furthermore, all the datasets are interesting and vary widely in subject matter. If nothing else, this book is an excellent source of examples one might use to illustrate a variety of statistical techniques. … it offers a lot of good places to start if one wants to analyze data. … The book comes hand-in-hand with an R package, HSAUR3, with all the data and the code used in the text. The book is thus fully reproducible. Overall, it provides a great way for a statistician to get started doing a wide variety of things in the R environment. It would be particularly useful, then, for working statisticians looking to change their software. The book cites all the relevant packages one might need, which is quite nice for those attempting to navigate the vast array of packages freely available, and is quite clear in its presentation of the code. Between this and the datasets, it makes for quite a valuable and enjoyable reference.”—The American Statistician, August 2015"… a handy primer for using R to perform standard statistical data analysis. … students, analysts, professors, and scientists: if you are looking to add R to your toolkit for analyzing data statistically, then this book will get you there."—Kendall Giles on his blog, September 2014Praise for the Second Edition:"I find the book by Everitt and Hothorn quite pleasant and bound to fit its purpose. The layout and presentation [are] nice. It should appeal to all readers as it contains a wealth of information about the use of R for statistical analysis. Included seasoned R users: When reading the first chapters, I found myself scribbling small lightbulbs in the margin to point out features of R I was not aware of. In addition, the book is quite handy for a crash introduction to statistics for (well-enough motivated) nonstatisticians."—International Statistical Review (2011), 79"… an extensive selection of real data analyzed with [R] … Viewed as a collection of worked examples, this book has much to recommend it. Each chapter addresses a specific technique. … the examples provide a wide variety of partial analyses and the datasets cover a diversity of fields of study. … This handbook is unusually free of the sort of errors spell checkers do not find."—MAA Reviews, April 2011Table of ContentsAn Introduction to R. Data Analysis Using Graphical Displays. Simple Inference. Conditional Inference. Analysis of Variance. Simple and Multiple Linear Regression. Logistic Regression and Generalized Linear Models. Density Estimation. Recursive Partitioning. Scatterplot Smoothers and Additive Models. Survival Analysis. Quantile Regression. Analyzing Longitudinal Data I. Analyzing Longitudinal Data II. Simultaneous Inference and Multiple Comparisons. Missing Values. Meta-Analysis. Bayesian Inference. Principal Component Analysis. Multidimensional Scaling. Cluster Analysis. Bibliography. Index.

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Book SynopsisThis book presents a unified and up-to-date introduction to ROC methodologies, covering both diagnosis (classification) and prediction. The emphasis is on the conceptual underpinning of ROC analysis and the practical implementation in diverse scientific fields. A plethora of examples accompany the methodologic discussion using standard statistical software such as R and STATA. The book arrives after two decades of intensive growth in both the methods and the applications of ROC analysis and presents a new synthesis. The authors provide a contemporary, integrated exposition of ROC methodology for both classification and prediction and include material on multiple-class ROC. This book avoids lengthy technical exposition and provides code and datasets in each chapter. ROC Analysis for Classification and Prediction in Practice is intended for researchers and graduate students, but will also be useful for those that use ROC analysis in diverse disciplines such as diagnostic medicineTable of Contents1. Introduction 2. Measures of Diagnostic and Predictive Performance 3. Statistical inference for the ROC curve 4. Comparing ROC curves 5. The ROC surface and k-class classification for k > 2 6. ROC regression 7. Missing data and errors-in-variables in ROC analysis

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Book SynopsisEmpirical research has now become an essential component of software engineering yet software practitioners and researchers often lack an understanding of how the empirical procedures and practices are applied in the field. Empirical Research in Software Engineering: Concepts, Analysis, and Applications shows how to implement empirical research processes, procedures, and practices in software engineering.Written by a leading researcher in empirical software engineering, the book describes the necessary steps to perform replicated and empirical research. It explains how to plan and design experiments, conduct systematic reviews and case studies, and analyze the results produced by the empirical studies. The book balances empirical research concepts with exercises, examples, and real-life case studies, making it suitable for a course on empirical software engineering. The author discusses the process of developing predictive models, such asTrade Review"In this book, Dr. Malhotra uses her breadth of software engineering experience and expertise to give the reader coverage of many aspects of empirical software engineering. She covers the essential techniques and concepts needed for a researcher to get started on empirical software engineering research, including metrics, experimental design, analysis and statistical techniques, threats to the validity of any research findings, and methods and tools for empirical software engineering research. … The book provides the reader with an introduction and overview of the field and is also backed by references to the literature, allowing the interested reader to follow up on the methods, tools, and concepts described."—From the Foreword by Mark Harman, University College LondonTable of ContentsIntroduction. Systematic Literature Reviews. Software Metrics. Experimental Design. Mining Data from Software Repositories. Data Analysis and Statistical Testing. Model Development and Interpretation. Validity Threats. Reporting Results. Mining Unstructured Data. Demonstrating Empirical Procedures. Tools for Analyzing Data. Appendix. References. Index.

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Book SynopsisMathematical Statistics: Basic Ideas and Selected Topics, Volume II presents important statistical concepts, methods, and tools not covered in the authors' previous volume. This second volume focuses on inference in non- and semiparametric models. It not only reexamines the procedures introduced in the first volume from a more sophisticated point of view but also addresses new problems originating from the analysis of estimation of functions and other complex decision procedures and large-scale data analysis.The book covers asymptotic efficiency in semiparametric models from the Le Cam and Fisherian points of view as well as some finite sample size optimality criteria based on LehmannScheffé theory. It develops the theory of semiparametric maximum likelihood estimation with applications to areas such as survival analysis. It also discusses methods of inference based on sieve models and asymptotic testing theory. The remainder of the book is devoted to Trade Review" . . . the authors have done a superb job of selecting topics comprising most of the essential knowledge needed formodern research. Furthermore, these modern topics are considered with greater depth and sophistication than is usual in a general purpose text. And throughout its pages the book does a good job of linking the mathematical developments to major examples. The choice of topics and examples, along with the depth of coverage are the most attractive features of this volume."~RobertW. Keener, University of MichiganTable of ContentsIntroduction and Examples. Tools for Asymptotic Analysis. Distribution-Free, Unbiased, and Equivariant Procedures. Inference in Semiparametric Models. Monte Carlo Methods. Nonparametric Inference for Functions of One Variable. Prediction and Machine Learning. Appendices. References. Indices.

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Book SynopsisThis book is among the first to present the mathematical models most commonly used to solve optimal execution problems and market making problems in finance. The Financial Mathematics of Market Liquidity: From Optimal Execution to Market Making presents a general modeling framework for optimal execution problemsinspired from the Almgren-Chriss approachand then demonstrates the use of that framework across a wide range of areas.The book introduces the classical tools of optimal execution and market making, along with their practical use. It also demonstrates how the tools used in the optimal execution literature can be used to solve classical and new issues where accounting for liquidity is important. In particular, it presents cutting-edge research on the pricing of block trades, the pricing and hedging of options when liquidity matters, and the management of complex share buy-back contracts.What sets this book apart from others is that it focuses onTrade Review"This excellent monograph covers the mathematical theory of market microstructure with particular emphasis in models of optimal execution and market making. Gueant’s book is a superb introduction to these topics for graduate students in mathematical finance or quants who want to work in execution algorithms or market-making strategies."—Jose A. Scheinkman, Charles and Lynn Zhang Professor of Economics, Columbia University, and Theodore Wells '29 Professor of Economics Emeritus, Princeton University"This is a very timely book that cuts across various fields (applied mathematics, operations research, and quantitative finance). Execution costs due to market illiquidity can significantly reduce returns on investment strategies and, for this reason, affect asset prices. It is therefore important to design trading strategies minimizing these costs and to account for their effect on prices. In the last decade, ‘quants’ and researchers in quantitative finance have made considerable progress on these issues, integrating in their models changes in the way financial markets work (e.g., the development of continuous limit order books, market fragmentation, dark pools, the automation of trading, etc.). "Olivier Guéant’s book takes stock of this effort by providing a rigorous and expert presentation of mathematical tools, models, and numerical methods developed in this area. I strongly recommend it for researchers and graduate students interested in how illiquidity costs affect trading strategies and should be accounted for in asset valuation problems."—Thierry Foucault, HEC Foundation Chair Professor of Finance, HEC, Paris"This book is a must-have for quantitative analysts working at algorithmic trading desks. Olivier Guéant could have written a sophisticated book dedicated to cutting-edge research. He rather decided to put his talent at the service of a far more difficult task: deliver a clear view of modern algorithmic trading to strats or quants having decent scientific training. Scientists will find here all the needed keys to control the intraday risk of their trading models, improving their overall efficiency. Covering brokerage algorithms, market making, hedging, and share buyback techniques, this book is the definitive reference for algorithm builders.Moreover, Olivier links algorithmic trading with market microstructure during the first chapter of the book, including interesting thoughts on corporate bonds trading. On the other hand, he provides a nice introduction to mathematical economics in the Appendix. This book is resolutely more than a bunch of equations thrown on blank pages. I consider it an important step forward in the building of the mathematics of market microstructure."—Charles-Albert Lehalle, Senior Research Advisor, Capital Fund ManagementTable of ContentsIntroduction. Optimal Liquidation. Liquidity in Pricing Models. Market Making.

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Book SynopsisIntroduction to Functional Data Analysis provides a concise textbook introduction to the field. It explains how to analyze functional data, both at exploratory and inferential levels. It also provides a systematic and accessible exposition of the methodology and the required mathematical framework.The book can be used as textbook for a semester-long course on FDA for advanced undergraduate or MS statistics majors, as well as for MS and PhD students in other disciplines, including applied mathematics, environmental science, public health, medical research, geophysical sciences and economics. It can also be used for self-study and as a reference for researchers in those fields who wish to acquire solid understanding of FDA methodology and practical guidance for its implementation. Each chapter contains plentiful examples of relevant R code and theoretical and data analytic problems.The material of the book can be roughly divided into four parts of approximaTrade Review"This well-written book provides a great and intuitive introduction to functional data analysis (FDA) which has emerged as an important area in statistics and found tons of scientific applications...This book succeeds at introducing this novel statistical concept and methodology while keeps the level of mathematical and statistical sophistication required to understand at the level of an introductory graduate-level course, which makes for pleasant reading. A nice feature of the book is its strong focus on implementation using R, which makes it a great candidate of textbooks or reference books for (master-level) graduate students and applied researchers...Some unique features of this book as compared to existing ones include (1) its strong focus on implementation using R; (2) chapters on Sparse FDA, generalized functional linear models, functional time series, and spatial functional data; (3) well-designed exercises that can be used as homework problems." ~Xianyang Zhang, Texas A&M University"The main advantage of the book is its emphasis introducing the material through realistic examples and computational tools, while also providing mathematical guidance for the methodologies. Also, important topics like functional time series and spatial functional data are not adequately covered in comparable texts like Ramsay and Silverman, Ramsay and Hooker, Ferraty and Vieu, and Hsing and Eubank. In that respect, the book offers additional and practically relevant material and perspective." ~Debashis Paul, University of California, Davis"The classic tools from the field of functional data analysis are introduced comprehensively and immediately put into a framework of potential application. I would probably advise any reader that is new to functional data analysis to start by reading this book." ~Claudia Klüppelberg, Technische Universität München"Being more advanced and up to date than the Ramsay and Silverman, it complements various topics that are just briefly mentioned or not covered at all by Ramsay and Silverman." ~Laura Sangali, Politecnico di Milano"As a relatively young subfield of statistics, functional data analysis (FDA) has not had a large glut of textbooks pertaining to it. The most famous of the FDA books is the classic text by J. O. Ramsay and B. W. Silverman [Functional data analysis, Springer Ser. Statist., Springer, New York, 1997; second edition, 2005; MR2168993], which introduced many statisticians to the area. Ramsay and Silverman [Applied functional data analysis, Springer Ser. Statist., Springer, New York, 2002; MR1910407] provided a useful collection of FDA case studies, and Ramsay, G. Hooker and S. Graves [Func-tional data analysis with R and MATLAB, Use R, Springer, New York, 2009, doi:10. 1007/978-0-387-98185-7] presented R and MATLAB code for analyzing real functional data sets. [F. Ferraty and P. Vieu, Nonparametric functional data analysis, Springer Ser. Statist., Springer, New York, 2006; MR2229687] and [T. Hsing and R. L. Eubank, Theoretical foundations of functional data analysis, with an introduction to linear opera- tors, Wiley Ser. Probab. Stat., Wiley, Chichester, 2015; MR3379106] are well-respected theoretical presentations of FDA.This book by Kokoszka and Reimherr provides a nice mix of foundational material, accessible theory, and practical examples (including much R code). It is a valuable addition to the FDA literature, and is perhaps an ideal choice of a course textbook for either an undergraduate or graduate course in FDA, whereas several of the other textbooks are more valuable as references for researchers and practitioners than as tutorials for learners. At the end of each chapter is a nice variety of problems that instructors could use for homework assignments.Chapter 1 introduces basic terminology related to FDA, such as the ubiquitous tool of basis expansion and the distinction between dense and sparse functional data. Summary statistics and plots (sample mean and covariance functions, principal components analysis (PCA), functional boxplots) for FDA are briey presented. Chapter 2 continues basic FDA topics with a discussion of derivative information, penalized smoothing, and alignment/registration of curves.The theoretical underpinnings of FDA are presented quickly in Chapter 3, where topics such as square integrable functions, random functions following some distribution, and operator theory are defined briey. A fuller coverage of theoretical concerns is saved for (the optional in a course setting) Chapters 10 and 11. The heart of the book is Chapters 4 through 9, which cover functional linear models in detail, before moving on to specialized FDA topics such as sparse FDA, functionaltime series, and spatial functional data. Scalar-on-function regression, in which the response is a scalar and the predictor is a function, is treated in Chapter 4, and illustrated via the use of the refund package in R. Nonlinear scalar-on-function regression is briey mentioned. Chapter 5 covers both the function-on-scalar regression case and the fully functional regression model in which both response and predictor are functions. Testing and validation of the functional linear model are also shown. Chapter 6 covers functional generalized linear models (GLMs) which have a nonnormal scalar response and a functional predictor. The somewhatnebulous situation with functional-response GLMs is briey covered as well. The next chapter deals with sparse functional data, and presents methods for mean function estimation, covariance function estimation, PCA, and regression in the sparse case when relatively few points are measured for each observed curve. Functional time series occur when the sample functions are observed sequentially over time rather than cross-sectionally. The assumption of independent functional data fails in this case, and Chapter 8 presents a functional autoregressive model for such data that can be used for forecasting. Spatial functional data may commonly be encountered in geostatistics when curves are observed both over time and at various spatial locations. Chapter 9 discusses models for such data and prediction using functional kriging. Chapter 12 discusses treating a functional data set as a sample from some population of functions and performing inference on the population. Of particular interest are the methods presented for formal hypothesis tests and confidence bands about the population mean function.Clustering and classification of functional data are not discussed in detail in thisbook, nor is FDA on manifolds, although references are given to guide readers to recentresearch in these areas."~David Benner HitchcockTable of ContentsFirst steps in the analysis of functional data Basis expansions Sample mean and covariance Principal component functions Analysis of BOA stock returns Diffusion tensor imaging Problems Further topics in exploratory FDA Derivatives Penalized smoothing Curve alignment Further reading Problems Mathematical framework for functional data Square integrable functions Random functions Linear transformations Scalar- on - function regressionExamples Review of standard regression theory Difficulties specific to functional regression Estimation through a basis expansion Estimation with a roughness penalty Regression on functional principal components Implementation in the refund package Nonlinear scalar-on-function regression Problems Functional response models Least squares estimation and application to angular motionPenalized least squares estimation Functional regressors Penalized estimation in the refund package Estimation based on functional principal components Test of no effectVerification of the validity of a functional linear model Extensions and further reading Problems Functional generalized linear models Background Scalar-on-function GLM's Functional response GLM Implementation in the refund package Application to DTI Further reading Problems Sparse FDA Introduction Mean function estimationCovariance function estimation Sparse functional PCA Sparse functional regression Problems Functional time seriesFundamental concepts of time series analysis Functional autoregressive process Forecasting with the Hyndman-Ullah methodForecasting with multivariate predictors Long-run covariance function Testing stationarity of functional time series Generation and estimation of the FAR(1) model using package fda Conditions for the existence of the FAR(1) process Further reading and other topics Problems Spatial functional data and modelsFundamental concepts of spatial statistics Functional spatial fields Functional kriging Mean function estimation Implementation in the R package geofd Other topics and further reading Problems Elements of Hilbert space theoryHilbert space Projections and orthonormal sets Linear operators Basics of spectral theory Tensors ProblemsRandom functions Random elements in metric spaces Expectation and covariance in a Hilbert space Gaussian functions and limit theorems Functional principal components ProblemsInference from a random sampleConsistency of sample mean and covariance functions Estimated functional principal components Asymptotic normality Hypothesis testing about the mean Confidence bands for the mean Application to BOA cumulative returns Proof of Theorem Problems

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Book SynopsisGeneralized Linear Mixed Models: Modern Concepts, Methods, and Applications (2nd edition) presents an updated introduction to linear modeling using the generalized linear mixed model (GLMM) as the overarching conceptual framework. For students new to statistical modeling, this book helps them see the big picture linear modeling as broadly understood and its intimate connection with statistical design and mathematical statistics. For readers experienced in statistical practice, but new to GLMMs, the book provides a comprehensive introduction to GLMM methodology and its underlying theory.Unlike textbooks that focus on classical linear models or generalized linear models or mixed models, this book covers all of the above as members of a unified GLMM family of linear models. In addition to essential theory and methodology, this book features a rich collection of examples using SAS software to illustrate GLMM practice. This second edition is updated to reflect les

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Book SynopsisElementary Probability with Applications, Second Edition shows students how probability has practical uses in many different fields, such as business, politics, and sports. In the book, students learn about probability concepts from real-world examples rather than theory.The text explains how probability models with underlying assumptions are used to model actual situations. It contains examples of probability models as they relate to: Bloc voting Population genetics Doubling strategies in casinos Machine reliability Airline management Cryptology Blood testing Dogs resembling owners Drug detection Jury verdicts Coincidences Number of concert hall aisles 2000 U.S. presidential election Points after deuce in tennis Tests regarding intelligent dogs Music composition Based on the author's course at TheTrade ReviewPraise for the First Edition:"It is desirable that all citizens have an elementary understanding of probability [so] that they better appreciate the uncertainty that surrounds them. Anyone teaching such citizens should consider using this book because, through its applications, it conveys something of the power of probability."—D.V. Lindley, Mathematical Gazette, November 2006"This book was surprisingly refreshing and did a great job using real situations to motivate techniques for calculating discrete probabilities."—Technometrics, August 2006"The problems are graded from fairly straightforward to very challenging and the book is, for this reason at least, a welcome addition to the literature."—MAA Reviews, October 2005"This book would be excellent for a minicourse at a higher level of high school as well as a semester course at the college level."—Larry White, National Council of Teachers of Mathematics (NCTM), October 2005"The chosen approach is practical and entertaining. The book is a useful tool for teachers and anybody interested in basic ideas and applications of classical probability theory."—EMS Newsletter, June 2005Table of ContentsBasic Concepts in Probability. Conditional Probability and the Multiplication Rule. Independence. Combining the Addition and Multiplication Rules. Combining the Addition and Multiplication Rules—Applications. Random Variables, Distributions, and Expected Values. Joint Distributions and Conditional Expectations. Sampling without Replacement. Sampling with Replacement. Sampling with Replacement (Continued). Binomial Tests. Appendix. Short Answers to Selected Exercises. Bibliography. Index.

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Book SynopsisData Analysis Using Stata, Third Edition is a comprehensive introduction to both statistical methods and Stata. Beginners will learn the logic of data analysis and interpretation and easily become self-sufficient data analysts. Readers already familiar with Stata will find it an enjoyable resource for picking up new tips and tricks.The book is written as a self-study tutorial and organized around examples. It interactively introduces statistical techniques such as data exploration, description, and regression techniques for continuous and binary dependent variables. Step by step, readers move through the entire process of data analysis and in doing so learn the principles of Stata, data manipulation, graphical representation, and programs to automate repetitive tasks. This third edition includes advanced topics, such as factor-variables notation, average marginal effects, standard errors in complex survey, and multiple imputation in a way, that beginners of both data analysis and Stata can understand.Using data from a longitudinal study of private households, the authors provide examples from the social sciences that are relatable to researchers from all disciplines. The examples emphasize good statistical practice and reproducible research. Readers are encouraged to download the companion package of datasets to replicate the examples as they work through the book. Each chapter ends with exercises to consolidate acquired skills. Table of ContentsThe First Time. Working with Do-Files. The Grammar of Stata. General Comments on the Statistical Commands. Creating and Changing Variables. Creating and Changing Graphs. Describing and Comparing Distributions. Statistical Inference. Introduction to Linear Regression. Regression Models for Categorical Dependent Variables. Reading and Writing Data. Do-Files for Advanced Users and User-Written Programs. Around Stata. References. Indices.

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Book SynopsisOver the past several decades, item response theory (IRT) and item response modeling (IRM) have become increasingly popular in the behavioral, educational, social, business, marketing, clinical, and health sciences. In this book, Raykov and Marcoulides begin with a nontraditional approach to IRT and IRM that is based on their connections to classical test theory, (nonlinear) factor analysis, generalized linear modeling, and logistic regression. Application-oriented discussions follow next. These cover the one-, two-, and three-parameter logistic models, polytomous item response models (with nominal or ordinal items), item and test information functions, instrument construction and development, hybrid models, differential item functioning, and an introduction to multidimensionalIRT and IRM. The pertinent analytic and modeling capabilities of Stata are thoroughly discussed, highlighted, and illustrated on empirical examples from behavioral and social research.Table of ContentsNotation and typography. What is item response theory and item response modeling? Two basic functions for item response theory and item response. Classical test theory, factor analysis, and their connections to item response theory Generalized linear modeling, logistic regression, nonlinear factor analysis, and their links to item response theory and item response modeling. Fundamentals of item response theory and item response modeling. First applications of Stata for item response modeling. Item response theory model fitting and estimation. Information functions and test characteristic curves Instrument construction and development using information functions. Differential item functioning. Polytomous item response models and hybrid models. Introduction to multidimensional item response theory and modeling

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Book SynopsisStata Tips provides concise and insightful notes about commands, features, and tricks that will help you obtain a deeper understanding of Stata.The book comprises the contributions of the Stata community that have appeared in the Stata Journal since 2003. Each tip is a brief article that provides practical advice on using Stata. With tips covering a breadth of topics in statistics, graphics, data management, and programming, both new and experienced Stata users are sure to find tips that will be useful in their research.

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Book SynopsisMaximum Likelihood Estimation with Stata, Fifth Edition is the essential reference and guide for researchers in all disciplines who wish to write maximum likelihood (ML) estimators in Stata. Beyond providing comprehensive coverage of Stata’s commands for writing ML estimators, the book presents an overview of the underpinnings of maximum likelihood and how to think about ML estimation.The fifth edition includes a new second chapter that demonstrates the easy-to-use mlexp command. This command allows you to directly specify a likelihood function and perform estimation without any programming.The core of the book focuses on Stata's ml command. It shows you how to take full advantage of ml’s noteworthy features: Linear constraints Four optimization algorithms (Newton–Raphson, DFP, BFGS, and BHHH) Observed information matrix (OIM) variance estimator Outer product of gradients (OPG) variance estimator Huber/White/sandwich robust variance estimator Cluster–robust variance estimator Complete and automatic support for survey data analysis Direct support of evaluator functions written in Mata When appropriate options are used, many of these features are provided automatically by ml and require no special programming or intervention by the researcher writing the estimator.In later chapters, you will learn how to take advantage of Mata, Stata's matrix programming language. For ease of programming and potential speed improvements, you can write your likelihood-evaluator program in Mata and continue to use ml to control the maximization process. A new chapter in the fifth edition shows how you can use the moptimize() suite of Mata functions if you want to implement your maximum likelihood estimator entirely within Mata.In the final chapter, the authors illustrate the major steps required to get from log-likelihood function to fully operational estimation command. This is done using several different models: logit and probit, linear regression, Weibull regression, the Cox proportional hazards model, random-effects regression, and seemingly unrelated regression. This edition adds a new example of a bivariate Poisson model, a model that is not available otherwise in Stata.The authors provide extensive advice for developing your own estimation commands. With a little care and the help of this book, users will be able to write their own estimation commands---commands that look and behave just like the official estimation commands in Stata.Whether you want to fit a special ML estimator for your own research or wish to write a general-purpose ML estimator for others to use, you need this book.Table of ContentsTheory and practice The likelihood-maximization problem Likelihood theory The maximization problem Estimation with mlexp Syntax Normal linear regression Initial values Restricted parameters Robust standard errors The probit model Specifying derivatives Additional estimation features Wrapping up Introduction to ml The probit mode Normal linear regression Robust standard errors Weighted estimation Other features of method-gf0 evaluators Limitations Overview of ml The terminology of ml Equations in ml Likelihood-evaluator methods Tools for the ml programmer Common ml options Maximizing your own likelihood functions Appendix: More about scalar parameters Method lf The linear-form restrictions Examples The importance of generating temporary variables as doubles Problems you can safely ignore Nonlinear specifications The advantages of lf in terms of execution speed Methods lf0, lf1, and lf2 Comparing these methods Outline of evaluators of methods lf0, lf1, and lf2 Summary of methods lf0, lf1, and lf2 Examples Methods d0, d1, and d2 Comparing these methods Outline of method d0, d1, and d2 evaluators Summary of methods d0, d1, and d2 Panel-data likelihoods Other models that do not meet the linear-form restrictions Debugging likelihood evaluators ml check Using the debug methods ml trace Setting initial values ml search ml plot ml init Interactive maximization The iteration log Pressing the Break key Maximizing difficult likelihood functions Final results Graphing convergence Redisplaying output Writing do-files to maximize likelihoods The structure of a do-file Putting the do-file into production Writing ado-files to maximize likelihoods Writing estimation commands The standard estimation-command outline Outline for estimation commands using ml Using ml in noninteractive mode Advice Writing ado-files for survey data analysis Program properties Writing your own predict command Mata-based likelihood evaluators Introductory examples Evaluator function prototypes Utilities Random-effects linear regression Ado-file considerations Mata’s moptimize() function Introductory examples Restricting the estimation sample Estimation preliminaries Estimation Results Estimation commands Regression redux Other examples The logit model The probit model Normal linear regression The Weibull model The Cox proportional hazards model The random-effects regression model The seemingly unrelated regression model A bivariate Poisson regression model Epilogue Syntax of mlexp Syntax of ml Syntax of moptimize() Likelihood-evaluator checklists Method lf Method d0 Method d1 Method d2 Method lf0 Method lf1 Method lf2 Listing of estimation commands The logit model The probit model The normal model The Weibull model The Cox proportional hazards model The random-effects regression model The seemingly unrelated regression model A bivariate Poisson regression model References

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Book SynopsisWhat is latent class analysis? If you asked that question thirty or forty years ago you would have gotten a different answer than you would today. Closer to its time of inception, latent class analysis was viewed primarily as a categorical data analysis technique, often framed as a factor analysis model where both the measured variable indicators and underlying latent variables are categorical. Today, however, it rests within much broader mixture and diagnostic modeling framework, integrating measured and latent variables that may be categorical and/or continuous, and where latent classes serve to define the subpopulations for whom many aspects of the focal measured and latent variable model may differ.For latent class analysis to take these developmental leaps required contributions that were methodological, certainly, as well as didactic. Among the leaders on both fronts was C. Mitchell “Chan” Dayton, at the University of Maryland, whose work in latent class analysis spanning several decades helped the method to expand and reach its current potential. The current volume in the Center for Integrated Latent Variable Research (CILVR) series reflects the diversity that is latent class analysis today, celebrating work related to, made possible by, and inspired by Chan’s noted contributions, and signaling the even more exciting future yet to come.

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Book Synopsis

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Book SynopsisR is a freely available, open-source statistical programming environment which provides powerful statistical analysis tools and graphics outputs. R is now used by a very wide range of people; biologists (the primary audience of this book), but also all other scientists and engineers, economists, market researchers and medical professionals. R users with expertise are constantly adding new associated packages, and the range already available is immense. This text works through a set of studies that collectively represent almost all the R operations that biology students need in order to analyse their own data. The material is designed to serve students from first year undergraduates through to those beginning post graduate levels. Chapters are organized around topics such as graphing, classical statistical tests, statistical modelling, mapping, and text parsing. Examples are based on real scientific studies, and each one covers the use of more R functions than those simply necessary to get a p-value or plot. The book walks the reader through the data analysis process, starting with very simple plots, and continuing through more complex analyses and programming. It shows how to deal with issues such as error messages that can be confronting for beginners, in order to set students up for a successful scientific career using R. Collectively the authors have a vast amount of teaching experience which they apply here to make the passage into R programming as gentle and easy as possible, whilst guiding the reader to tackle quite complicated programming.Table of Contents1: How to Use this Book 2: Installing and Running R 3: Very Basic R Syntax 4: First Simple Programs and Graphics 5: The Dataframe Concept 6: Plotting Biological Data in Various Ways 7: The Grammar of Graphics Family of Packages 8: Sets and Venn diagrams 9: Statistics: Choosing the Right Test 10: Commonly Used Measures and Statistical Tests 11: Regression and Correlation Analyses 12: Count Data as Response Variable 13: Analysis of Variance (ANOVA) 14: Analysis of Covariance (ANCOVA) 15: More Generalised Linear Modelling 16: Monte Carlo Tests and Randomisation 17: Principal Components Analysis 18: Species Abundance, Accumulation and Diversity Data 19: Survivorship 20: Dates and Julian Dates 21: Mapping and Parsing Text Input for Data 22: More on Manipulating Text 23: Phylogenies and Trees 24: Working with DNA Sequences and other character data 25: Spacing in Two Dimensions 26: Population Modelling Including Spatially Explicit Models 27: More on “apply” Family of Functions – Avoid Loops to get More Speed 28: Food webs and simple graphics 29: Adding Photographs 30: Standard Distributions in R 31: Reading and Writing Data to and from Files

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Book SynopsisEverything a student needs to ensure exam success Written by Chief examiners and experienced teachers. Revised and enhanced following user feedback on the 2001 Heinemann edition. Practice exam papers for foundation and higher, exactly matched to the new specification. Three revision exercises, featuring past exam questions, consolidate learning on groups of topics. examzone section gives tips, tests and techniques for exam preparation and the new controlled assessment.

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Book SynopsisThis book provides an essential introduction to Stochastic Programming, especially intended for graduate students. The book begins by exploring a linear programming problem with random parameters, representing a decision problem under uncertainty. Several models for this problem are presented, including the main ones used in Stochastic Programming: recourse models and chance constraint models. The book not only discusses the theoretical properties of these models and algorithms for solving them, but also explains the intrinsic differences between the models. In the book’s closing section, several case studies are presented, helping students apply the theory covered to practical problems. The book is based on lecture notes developed for an Econometrics and Operations Research course for master students at the University of Groningen, the Netherlands - the longest-standing Stochastic Programming course worldwide. Trade Review“The book is well written. The book will be of interest to mathematicians, engineers, economics and especially graduate students.” (I. M. Stancu-Minasian, zbMATH 1446.90118, 2020)Table of ContentsIntroduction.- Random Objective Functions.- Recourse Models.- Stochastic Mixed-integer Programming.- Chance Constraints.- Integrated Chance Constraints.- Assignments.- Case Studies.

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Book SynopsisThe new edition of this book provides an easily accessible introduction to the statistical analysis of network data using R. It has been fully revised and can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. The new edition of this book includes an overhaul to recent changes in igraph. The material in this book is organized to flow from descriptive statistical methods to topics centered on modeling and inference with networks, with the latter separated into two sub-areas, corresponding first to the modeling and inference of networks themselves, and then, to processes on networks. The book begins by covering tools for the manipulation of network data. Next, it addresses visualization and characterization of networks. The book then examines mathematical and statistical network modeling. This is followed by a special case of network modeling wherein the network topology must be inferred. Network processes, both static and dynamic are addressed in the subsequent chapters. The book concludes by featuring chapters on network flows, dynamic networks, and networked experiments. Statistical Analysis of Network Data with R, 2nd Ed. has been written at a level aimed at graduate students and researchers in quantitative disciplines engaged in the statistical analysis of network data, although advanced undergraduates already comfortable with R should find the book fairly accessible as well.Table of Contents1 Introduction.- 2 Manipulating Network Data.- 3 Visualizing Network Data.- 4 Descriptive Analysis of Network Graph Characteristics.- 5 Mathematical Models for Network Graphs.- 6 Statistical Models for Network Graphs.- 7 Network Topology Inference.- 8 Modeling and Prediction for Processes on Network Graphs.- 9 Analysis of Network Flow Data.- 10 Networked Experiments.- 11 Dynamic Networks.- Index.

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Book SynopsisThis book provides an extensive overview of the principles and methods of sample size calculation and recalculation in clinical trials. Appropriate calculation of the required sample size is crucial for the success of clinical trials. At the same time, a sample size that is too small or too large is problematic due to ethical, scientific, and economic reasons. Therefore, state-of-the art methods are required when planning clinical trials. Part I describes a general framework for deriving sample size calculation procedures. This enables an understanding of the common principles underlying the numerous methods presented in the following chapters. Part II addresses the fixed sample size design, where the required sample size is determined in the planning stage and is not changed afterwards. It covers sample size calculation methods for superiority, non-inferiority, and equivalence trials, as well as comparisons between two and more than two groups. A wide range of further topics is discussed, including sample size calculation for multiple comparisons, safety assessment, and multi-regional trials. There is often some uncertainty about the assumptions to be made when calculating the sample size upfront. Part III presents methods that allow to modify the initially specified sample size based on new information that becomes available during the ongoing trial. Blinded sample size recalculation procedures for internal pilot study designs are considered, as well as methods for sample size reassessment in adaptive designs that use unblinded data from interim analyses. The application is illustrated using numerous clinical trial examples, and software code implementing the methods is provided. The book offers theoretical background and practical advice for biostatisticians and clinicians from the pharmaceutical industry and academia who are involved in clinical trials. Covering basic as well as more advanced and recently developed methods, it is suitable for beginners, experienced applied statisticians, and practitioners. To gain maximum benefit, readers should be familiar with introductory statistics. The content of this book has been successfully used for courses on the topic.Trade Review“The R source code is shown by chapter, well-documented, and easy to find and follow as brief descriptions and necessary specifications for the function calls are given by means of comments. … a wide area of application fields is covered and exhaustive literature references for further reading are given. … The presentation of the material is very reader-friendly, easily accessible and pedagogical … . It is likewise highly recommended … . This is an effective and nicely written reference textbook.” (Oke Gerke, ISCB News, iscb.info, Vol. 72, December, 2021)Table of ContentsPart I Basics 1 Introduction 1.1 Background and outline 1.2 Examples 1.2.1 The ChroPac trial 1.2.2 The Parkinson trial 1.3 General considerations when calculating sample sizes 2 Statistical test and sample size calculation 2.1 The main principle of statistical testing 2.2 The main principle of sample size calculation Part II Sample size calculation 3 Comparison of two groups for normally distributed outcomes and test for difference or superiority 3.1 Background and notation 3.2 z-test 3.3 t-test 3.4 Analysis of covariance 3.5 Bayesian approach 3.5.1 Background 3.5.2 Methods 4 Comparison of two groups for continuous and ordered categorical outcomes and test for difference or superiority 4.1 Background and notation 4.2 Continuous outcomes 4.3 Ordered categorical outcomes 4.3.1 Assumption-free approach 4.3.2 Assuming proportional odds 5 Comparison of two groups for binary outcomes and test for difference and superiority 5.1 Background and notation 5.2 Asymptotic tests 5.2.1 Difference of rates as effect measure 5.2.2 Risk ratio as effect measure 5.2.3 Odds ratio as effect measure 5.2.4 Logistic regression 5.3 Exact unconditional tests 5.3.1 Background 5.3.2 Fisher-Boschloo test 6 Comparison of two groups for time-to-event outcomes and test for differences or superiority 6.1 Background and notation 6.1.1 Time-to-event data 6.1.2 Sample size calculation for time-to-event data 6.2 Exponentially distributed time-to-event data 6.3 Time-to-event data with proportional hazards 6.3.1 Approach of Schoenfeld 6.3.2 Approach of Freedman 7 Comparison of more than two groups and test for difference 7.1 Background and notation 7.2 Normally distributed outcomes 7.3 Continuous outcomes 7.4 Binary outcomes 7.4.1 Analysis with chi-square test 7.4.2 Analysis with Cochran-Armitage test 7.5 Time-to-event outcomes 8 Comparison of two groups and test for non-inferiority 8.1 Background and notation 8.2 Normally distributed outcomes 8.2.1 Difference of means 8.2.2 Ratio of means 8.3 Continuous and ordered categorical outcomes 8.4 Binary outcomes 8.4.1 Analysis with asymptotic tests 8.4.1.1 Difference of rates as effect measure 8.4.1.2 Risk ratio as effect measure 8.4.1.3 Odds ratio as effect measure 8.4.2 Exact unconditional tests 8.4.2.1 Background 8.4.2.2 Difference of rates as effect measure 8.4.2.3 Risk ratio as effect measure 8.4.2.4 Odds ratio as effect measure 8.5 Time-to-event outcomes 9 Comparison of three groups in the gold standard non-inferiority design 9.1 Background and notation 9.2 Net effect approach 9.3 Fraction effect approach 10 Comparison of two groups for normally distributed outcomes and test for equivalence 10.1 Background and notation 10.2 Difference of means 10.3 Ratio of means 11 Multiple comparisons 11.1 Background and notation 11.2 Generally applicable sample size calculation methods and applications 11.2.1 Methods 11.2.2 Applications 11.3 Multiple endpoints 11.3.1 Background and notation 11.3.2 Methods 11.4 More than two groups 11.4.1 Background and notation 11.4.2 Dunnett test 12 Assessment of safety 12.1 Background and notation 12.2 Testing hypotheses on the event probability 12.3 Estimating the occurrence probability of an event with specified precision 12.4 Observing at least one event 13 Cluster-randomized trials 13.1 Background and notation 13.2 Normally distributed outcomes 13.2.1 Cluster-level analysis 13.2.2 Individual-level analysis 13.2.3 Dealing with unequal cluster size 13.3 Other scale levels of the outcome 14 Multi-regional trials 14.1 Background and notation 14.2 Sample size calculation for demonstrating consistency of global results and results for a specified region 14.3 Sample size calculation for demonstrating a consistent trend across all regions 15 Integrated planning of phase II/III drug development programs 15.1 Background and notation 15.2 Optimizing phase II/III programs 16 Simulation-based sample size calculation Part III Sample size recalculation 17 Background Part IIIA Blinded sample size recalculation in internal pilot study designs 18 Background and notation 19 A general approach for controlling the type I error rate for blinded sample size recalculation 20 Comparison of two groups for normally distributed outcomes and test for difference or superiority 20.1 t-Test 20.1.1 Background and notation 20.1.2 Blinded variance estimation 20.1.3 Type I error rate 20.1.4 Power and sample size 20.2 Analysis of covariance 20.2.1 Background and notation 20.2.2 Blinded variance estimation 20.2.3 Type I error rate 20.2.4 Power and sample size 21 Comparison of two groups for binary outcomes and test for difference or superiority 21.1 Background and notation 21.2 Asymptotic tests 21.2.1 Difference of rates as effect measure 21.2.2 Risk ratio and odds ratio as effect measure 21.3 Fisher-Boschloo test 22 Comparison of two groups for normally distributed outcomes and test for non-inferiority 22.1 t-Test 22.1.1 Background and notation 22.1.2 Blinded variance estimation 22.1.3 Type I error rate 22.1.4 Power and sample size 22.2 Analysis of covariance 23 Comparison of two groups for binary outcomes and test for non-inferiority 23.1 Background and notation 23.2 Difference of rates as effect measure 23.3 Risk ratio and odds ratio as effect measure 24 Comparison of two groups for normally distributed outcomes and test for equivalence 25 Regulatory and operational aspects 26 Concluding remarks Part IIIB Unblinded sample size recalculation in adaptive designs 27 Background and notation 27.1 Group-sequential designs 27.2 Adaptive designs 27.2.1 Combination function approach 27.2.2 Conditional error function approach 28 Sample size recalculation based on conditional power 28.1 Background and notation 28.2 Using the interim estimate of the effect 28.3 Using the initially specified effect 28.4 Using prior information as well as the interim effect estimate 29 Sample size recalculation by optimization 30 Regulatory and operational aspects 31 Concluding remarks Appendix: Selected R software code References

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Book SynopsisThis book contains 296 exercises and solutions covering a wide variety of topics in linear model theory, including generalized inverses, estimability, best linear unbiased estimation and prediction, ANOVA, confidence intervals, simultaneous confidence intervals, hypothesis testing, and variance component estimation. The models covered include the Gauss-Markov and Aitken models, mixed and random effects models, and the general mixed linear model. Given its content, the book will be useful for students and instructors alike. Readers can also consult the companion textbook Linear Model Theory - With Examples and Exercises by the same author for the theory behind the exercises.Trade Review“This volume contains solutions to the book's exercises … Many of those exercises stand as useful applications of results stated in the theory volume. Some of them go one step beyond and extend the theoretical results. I found this to be a very interesting and unique feature of the book on linear models, making the whole set particularly useful for both graduate students and instructors.” (Vassilis G. S. Vasdekis, Mathematical Reviews, August 2022)Table of Contents1 A Brief Introduction.- 2 Selected Matrix Algebra Topics and Results.- 3 Generalized Inverses and Solutions to Sytems of Linear Equations.- 4 Moments of a Random Vector and of Linear and Quadratic Forms in a Random Vector.- 5 Types of Linear Models.- 6 Estimability.- 7 Least Squares Estimation for the Gauss-Markov Model.- 8 Least Squares Geometry and the Overall ANOVA.- 9 Least Squares Estimation and ANOVA for Partitioned Models.- 10 Constrained Least Squares Estimation and ANOVA.- 11 Best Linear Unbiased Estimation for the Aitken Model.- 12 Model Misspecification.- 13 Best Linear Unbiased Prediction.- 14 Distribution Theory.- 15 Inference for Estimable and Predictable Functions.- 16 Inference for Variance-Covariance Parameters.- 17 Empirical BLUE and BLUP.

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Book SynopsisThis popular textbook, now in a revised and expanded third edition, presents a comprehensive course in modern probability theory.Probability plays an increasingly important role not only in mathematics, but also in physics, biology, finance and computer science, helping to understand phenomena such as magnetism, genetic diversity and market volatility, and also to construct efficient algorithms. Starting with the very basics, this textbook covers a wide variety of topics in probability, including many not usually found in introductory books, such as: limit theorems for sums of random variables martingales percolation Markov chains and electrical networks construction of stochastic processes Poisson point process and infinite divisibility large deviation principles and statistical physics Brownian motion stochastic integrals and stochastic differential equations. The presentation is self-contained and mathematically rigorous, with the material on probability theory interspersed with chapters on measure theory to better illustrate the power of abstract concepts.This third edition has been carefully extended and includes new features, such as concise summaries at the end of each section and additional questions to encourage self-reflection, as well as updates to the figures and computer simulations. With a wealth of examples and more than 290 exercises, as well as biographical details of key mathematicians, it will be of use to students and researchers in mathematics, statistics, physics, computer science, economics and biology.Table of Contents1 Basic Measure Theory.- 2 Independence.- 3 Generating Functions.- 4 The Integral.- 5 Moments and Laws of Large Numbers.- 6 Convergence Theorems.- 7 Lp-Spaces and the Radon–Nikodym Theorem.- 8 Conditional Expectations.- 9 Martingales.- 10 Optional Sampling Theorems.- 11 Martingale Convergence Theorems and Their Applications.- 12 Backwards Martingales and Exchangeability.- 13 Convergence of Measures.- 14 Probability Measures on Product Spaces.- 15 Characteristic Functions and the Central Limit Theorem.- 16 Infinitely Divisible Distributions.- 17 Markov Chains.- 18 Convergence of Markov Chains.- 19 Markov Chains and Electrical Networks.- 20 Ergodic Theory.- 21 Brownian Motion.- 22 Law of the Iterated Logarithm.- 23 Large Deviations.- 24 The Poisson Point Process.- 25 The Itô Integral.- 26 Stochastic Differential Equations.- References.- Notation Index.- Name Index.- Subject Index.

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Book SynopsisThis textbook is a step-by-step guide for high school, community college, and undergraduate students who are taking a course in applied statistics and wish to learn how to use Excel to solve statistical problems. All of the statistics problems in this book come from the following fields of study: business, education, psychology, marketing, engineering and advertising. Students will learn how to perform key statistical tests in Excel without being overwhelmed by statistical theory. Each chapter briefly explains a topic and then demonstrates how to use Excel commands and formulas to solve specific statistics problems. The book offers guidance in using Excel in two different ways: (1) writing formulas (e.g., confidence interval about the mean, one-group t-test, two-group t-test, correlation) and (2) using Excel’s drop-down formula menus (e.g., simple linear regression, multiple correlations and multiple regression, and one-way ANOVA). Three practice problems are provided at the end of each chapter, along with their solutions in an appendix. An additional practice test allows readers to test their understanding of each chapter by attempting to solve a specific statistics problem using Excel; the solution to each of these problems is also given in an appendix. This book is a tool that can be used either by itself or along with any good statistics book.​Table of ContentsPreface.- Acknowledgements.- 1 Sample Size, Mean, Standard Deviation, and Standard Error of the Mean.- 2 Random Number Generator.- 3 Confidence Interval About the Mean Using the TINV Function and Hypothesis Testing.- 4 One-Group t-Test for the Mean.- 5 Two-Group t-Test of the Difference of the Means for Independent Groups.- 6 Correlation and Simple Linear Regression.- 7 Multiple Correlation and Multiple Regression.- 8 One-Way Analysis of Variance (ANOVA).- Appendix A: Answers to End-of-Chapter Practice Problems.- Appendix B: Practice Test.- Appendix C: Answers to Practice Test.- Appendix D: Statistical Formulas.- Appendix E: t-table.- Index.

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Book SynopsisThis richly illustrated book describes statistical extreme value theory for the quantification of natural hazards, such as strong winds, floods and rainfall, and discusses an interdisciplinary approach to allow the theoretical methods to be applied. The approach consists of a number of steps: data selection and correction, non-stationary theory (to account for trends due to climate change), and selecting appropriate estimation techniques based on both decision-theoretic features (e.g., Bayesian theory), empirical robustness and a valid treatment of uncertainties. It also examines and critically reviews alternative approaches based on stochastic and dynamic numerical models, as well as recently emerging data analysis issues and presents large-scale, multidisciplinary, state-of-the-art case studies. Intended for all those with a basic knowledge of statistical methods interested in the quantification of natural hazards, the book is also a valuable resource for engineers conducting risk analyses in collaboration with scientists from other fields (such as hydrologists, meteorologists, climatologists). Table of Contents1 E. Garnier: Extreme Events and History: for a better consideration of natural hazards.- 2 N. Bousquet and P. Bernardara: Introduction.- Part I Standard Extreme Value Theory.- 3 P. Bernardara and N. Bousquet: Probabilistic modeling and statistical quantification of natural hazards.- 4 N. Bousquet: Fundamental concepts of probability and statistics.- 5 M. Andreewsky and N. Bousquet: Collecting and analyzing data.- 6 A. Dutfoy: Univariate extreme value theory: practice and limitations.- Part II Elements of Extensive Statistical Analysis.- 7 J. Weiss and M. Andreewsky: Regional extreme value analysis.- 8 S. Parey, T. Hoang: Extreme values of non-stationary time series.- 9 A. Dutfoy: Multivariate extreme value theory: practice and limits.- 10 S., T. Hoang and N. Bousquet: Stochastic and physics-based simulation of extreme situations.- 11 N. Bousquet: Bayesian extreme value theory.- 12 M. Andreewsky, P. Bernardara, N. Bousquet, A. Dutfoy and S. Parey: Perspectives.- Part III Detailed Case Studies on Natural Hazards.- 13 P. Bernardara: Predicting extreme ocean swells.- 14 M. Andreewsky: Predicting storm surges.- 15 S. Parey: Forecasting extreme winds.- 16 N. Roche and A. Dutfoy: Conjunction of rainfall in neighboring watersheds.- 17 A. Sibler and A. Dutfoy: Conjunction of a flood and a storm.- 18 E. Paquet: SCHADEX: an alternative to extreme value statistics in hydrology.- Appendix A.- Appendix B.- References.- Index.

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Book SynopsisThis book presents covariance matrix estimation and related aspects of random matrix theory. It focuses on the sample covariance matrix estimator and provides a holistic description of its properties under two asymptotic regimes: the traditional one, and the high-dimensional regime that better fits the big data context. It draws attention to the deficiencies of standard statistical tools when used in the high-dimensional setting, and introduces the basic concepts and major results related to spectral statistics and random matrix theory under high-dimensional asymptotics in an understandable and reader-friendly way. The aim of this book is to inspire applied statisticians, econometricians, and machine learning practitioners who analyze high-dimensional data to apply the recent developments in their work.Table of ContentsForeword.- 1 Introduction.- 2 Traditional Estimators and Standard Asymptotics.- 3 Finite Sample Performance of Traditional Estimators.- 4 Traditional Estimators and High-Dimensional Asymptotics.- 5 Summary and Outlook.- Appendices.

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Book SynopsisThis Fourth Edition updates the "Solutions Manual for Econometrics" to match the Sixth Edition of the Econometrics textbook. It adds problems and solutions using latest software versions of Stata and EViews. Special features include empirical examples replicated using EViews, Stata as well as SAS. The book offers rigorous proofs and treatment of difficult econometrics concepts in a simple and clear way, and provides the reader with both applied and theoretical econometrics problems along with their solutions. These should prove useful to students and instructors using this book.Table of ContentsWhat Is Econometrics?.- A Review of Some Basic Statistical Concepts.- Simple Linear Regression.- Multiple Regression Analysis.- Violations of the Classical Assumptions.- Distributed Lags and Dynamic Models.- The General Linear Model: The Basics.- Regression Diagnostics and Specification Tests.- Generalized Least Squares.- Seemingly Unrelated Regressions.- Simultaneous Equations Model.- Pooling Time-Series of Cross-Section Data.- Limited Dependent Variables.- Time-Series Analysis.

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Book SynopsisThis book discusses a link between statistical theory and quantum theory based on the concept of epistemic processes. The latter are processes, such as statistical investigations or quantum mechanical measurements, that can be used to obtain knowledge about something. Various topics in quantum theory are addressed, including the construction of a Hilbert space from reasonable assumptions and an interpretation of quantum states. Separate derivations of the Born formula and the one-dimensional Schrödinger equation are given. In concrete terms, a Hilbert space can be constructed under some technical assumptions associated with situations where there are two conceptual variables that can be seen as maximally accessible. Then to every accessible conceptual variable there corresponds an operator on this Hilbert space, and if the variables take a finite number of values, the eigenspaces/eigenvectors of these operators correspond to specific questions in nature together with sharp answers to these questions. This paves a new way to the foundations of quantum theory. The resulting interpretation of quantum mechanics is related to Hervé Zwirn's recent Convivial Solipsism, but it also has some relations to Quantum Bayesianism and to Rovelli's relational quantum mechanics. Niels Bohr's concept of complementarity plays an important role. Philosophical implications of this approach to quantum theory are discussed, including consequences for macroscopic settings.The book will benefit a broad readership, including physicists and statisticians interested in the foundations of their disciplines, philosophers of science and graduate students, and anyone with a reasonably good background in mathematics and an open mind.Table of Contents1. The epistemic view upon science.- 2. Statistical inference.- 3. Inference in an epistemic process.- 4. Towards quantum theory.- 5. Aspects of quantum theory.- 6. Macroscopic consequences.

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Book SynopsisThis textbook covers all central activities of data warehousing and analytics, including transformation, preparation, aggregation, integration, and analysis. It discusses the full spectrum of the journey of data from operational/transactional databases, to data warehouses and data analytics; as well as the role that data warehousing plays in the data processing lifecycle. It also explains in detail how data warehouses may be used by data engines, such as BI tools and analytics algorithms to produce reports, dashboards, patterns, and other useful information and knowledge.The book is divided into six parts, ranging from the basics of data warehouse design (Part I - Star Schema, Part II - Snowflake and Bridge Tables, Part III - Advanced Dimensions, and Part IV - Multi-Fact and Multi-Input), to more advanced data warehousing concepts (Part V - Data Warehousing and Evolution) and data analytics (Part VI - OLAP, BI, and Analytics).This textbook approaches data warehousing from the case study angle. Each chapter presents one or more case studies to thoroughly explain the concepts and has different levels of difficulty, hence learning is incremental. In addition, every chapter has also a section on further readings which give pointers and references to research papers related to the chapter. All these features make the book ideally suited for either introductory courses on data warehousing and data analytics, or even for self-studies by professionals. The book is accompanied by a web page that includes all the used datasets and codes as well as slides and solutions to exercises.Table of Contents1. Introduction.- Part I: Star Schema.- 2. Simple Star Schemas.- 3. Creating Facts and Dimensions: More Complex Processes.- Part II: Snowflake and Bridge Tables.- 4. Hierarchies.- 5. Bridge Tables.- 6. Temporal Data Warehousing.- Part III: Advanced Dimension.- 7. Determinant Dimensions.- 8. Junk Dimensions.- 9. Dimension Keys.- 10. One-Attribute Dimensions.- Part IV: Multi-Fact and Multi-Input.- 11. Multi-Fact Star Schemas.- 12. Slicing a Fact.- 13. Multi-Input Operational Databases.- Part V: Data Warehousing Granularity and Evolution.- 14. Data Warehousing Granularity and Levels of Aggregation.- 15. Designing Lowest-Level Star Schemas.- 16. Levels of Aggregation: Adding and Removing Dimensions.- 17. Levels of Aggregation and Bridge Tables.- 18. Active Data Warehousing.- Part VI: OLAP, Business Intelligence, and Data Analytics.- 19. Online Analytical Processing (OLAP).- 20. Pre- and Post-Data Warehousing.- 21. Data Analytics for Data Warehousing.

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Book SynopsisThis book introduces readers to benchmarking techniques in the stochastic environment, primarily stochastic data envelopment analysis (DEA), and provides stochastic models in DEA for the possibility of variations in inputs and outputs. It focuses on the application of theories and interpretations of the mathematical programs, which are combined with economic and organizational thinking. The book’s main purpose is to shed light on the advantages of the different methods in deterministic and stochastic environments and thoroughly prepare readers to properly use these methods in various cases. Simple examples, along with graphical illustrations and real-world applications in industry, are provided for a better understanding. The models introduced here can be easily used in both theoretical and real-world evaluations. This book is intended for graduate and PhD students, advanced consultants, and practitioners with an interest in quantitative performance evaluation.Table of Contents1. Benchmarking.- 2. An Introduction to Data Envelopment Analysis.- 3. Probability Theory.- 4. Stochastic Data Envelopment Analysis.- 5. Stochastic Network Data Envelopment Analysis.- 6. Stochastic Scale Elasticity.

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Book SynopsisThis book provides a practical introduction to mathematics for economics using R software. Using R as a basis, this book guides the reader through foundational topics in linear algebra, calculus, and optimization. The book is organized in order of increasing difficulty, beginning with a rudimentary introduction to R and progressing through exercises that require the reader to code their own functions in R. All chapters include applications for topics in economics and econometrics. As fully reproducible book, this volume gives readers the opportunity to learn by doing and develop research skills as they go. As such, it is appropriate for students in economics and econometrics.Table of Contents1. Introduction to R.- 2. Linear Algebra.- 3. Functions of one variable.- 4. Dierential Calculus.- 5. Integral Calculus.- 6. Multivariable Calculus.- 7. Constrained Optimization.- 8. Trigonometry.- 9. Complex numbers.- 10. Difference equations.- 11. Differential equations.

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Book SynopsisThis book explores recent topics in quantitative finance with an emphasis on applications and calibration to time-series. This last aspect is often neglected in the existing mathematical finance literature while it is crucial for risk management. The first part of this book focuses on switching regime processes that allow to model economic cycles in financial markets. After a presentation of their mathematical features and applications to stocks and interest rates, the estimation with the Hamilton filter and Markov Chain Monte-Carlo algorithm (MCMC) is detailed. A second part focuses on self-excited processes for modeling the clustering of shocks in financial markets. These processes recently receive a lot of attention from researchers and we focus here on its econometric estimation and its simulation. A chapter is dedicated to estimation of stochastic volatility models. Two chapters are dedicated to the fractional Brownian motion and Gaussian fields. After a summary of their features, we present applications for stock and interest rate modeling. Two chapters focuses on sub-diffusions that allows to replicate illiquidity in financial markets. This book targets undergraduate students who have followed a first course of stochastic finance and practitioners as quantitative analyst or actuaries working in risk management.Trade Review“Hainaut has written a book which in such panorama has a position of its own and which should be considered with great interest. … the book should definitely be considered an excellent and warmly recommended read. It is likely that it will be soon become a reference for those interested in modern topics and for young researchers in particular.” (Gianluca Cassese, zbMATH 1512.91001, 2023)Table of ContentsPreface.- Acknowledgements.- Notations.- 1. Switching Models: Properties and Estimation.- 2. Estimation of Continuous Time Processes by Markov Chain Monte Carlo.- 3. Particle Filtering and Estimation.- 4. Modeling of Spillover Effects in Stock Markets.- 5. Non-Markov Models for Contagion and Spillover.- 6. Fractional Brownian Motion.- 7. Gaussian Fields for Asset Prices.- 8. Lévy Interest Rate Models With a Long Memory.- 9. Affine Volterra Processes and Rough Models.- 10. Sub-Diffusion for Illiquid Markets.- 11. A Fractional Dupire Equation for Jump-Diffusions.- References.

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Book SynopsisThis book provides an overview of the emerging topics in biostatistical theories and methods through their applications to evidence-based global health research and decision-making. It brings together some of the top scholars engaged in biostatistical method development on global health to highlight and describe recent advances in evidence-based global health applications. The volume is composed of five main parts: data harmonization and analysis; systematic review and statistical meta-analysis; spatial-temporal modeling and disease mapping; Bayesian statistical modeling; and statistical methods for longitudinal data or survival data. It is designed to be illuminating and valuable to both expert biostatisticians and to health researchers engaged in methodological applications in evidence-based global health research. It is particularly relevant to countries where global health research is being rigorously conducted.Table of Contents1. Harmonization of Longitudinal Population Data: evidence from three rural health and demographic surveillance system nodes in South Africa.- 2. Adjusting for Selection Bias in Assessing the Efficacy of Health Inputs on Birth Outcome: Evidence from South-Saharan Africa.- 3. An Indirect Assessment of the Effect of Anthropogenic Activities on the Ecology of the Intermediate Snail Host for Schistosoma Haematobium.- 4. Diagonal Reference Modeling of Effects of Couples' Educational Differences on Women's Health Care Utilization in Sub-Saharan Africa - Gebrenegus Ghilagaber, Michael Carlson.- 5. Sequential Modeling of Parity Progression Ratios in Sub-Saharan Africa.- 6. Evidence-informed Public Health, Systematic Reviews, and Meta-analysis.- 7. Meta-analysis Methods and Empirical Comparison of Aggregate Data and Individual Participant Data Results from Sample Survey Data.- 8. Statistical Meta-analysis and its Efficience Between Summary Statistics and Individual Participant-level Data: A Monte-Carlo simulation study.- 9. Multivariate Disease Mapping for Multiple Health Outcomes.- 10. Measuring Spatial Dependence of Non-communicable Diseases in South Africa.- 11. Mapping Health Outcomes in Sub-Saharan African Region Using Survey Data, Adjusting for Survey Data - Sheyla Rodrigues Cassy.- 12. Spatial Multi-criteria Decision Analysis in Health Sciences: Fifteen years of applications and trends.- 13. Estimating Determinants of Stage at Diagnosis of Breast Cancer Prevalence in Western Nigeria Using Bayesian Logistic Regression.- 14. Dynamic Bayesian Adjustment of Educational Gradients in Divorce Risks: Disentangling causation and misclassification.- 15. Bayesian Dynamic Models for Time-Varying Outcomes: Applications to a patient cohort on ART.- 16. Suicide Ideation and Associated Factors Among School-going Adolescents in Namibia: A Multilevel logistic regression.- 17. Bayesian Inference in the Extended Generalized Gamma Model and its Special Cases: With applications on demographic and health survey data from Sub-Saharan Africa.- 18. Changing Effects of Covariates on Childhood Mortality in Sub-Saharan Africa: A dynamic Bayesian survival modeling approach.- 19. Group Outliers and Influence Assessments in Clustered Survival Data Modeling.- 20. Joint Modeling of Competing Risks Survival and Longitudinal Data

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Book SynopsisThis book constitutes the refereed proceedings of the 17th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2022, held in Cartagena de Indias, Colombia, in November 2022. The 33 full and 4 short papers presented were carefully reviewed and selected from 67 submissions. The papers are organized in the following topical sections: applications of AI; ethics and smart city; green and sustainable AI; machine learning; natural language processing; robotics and computer vision; simulation and forecasting.Table of ContentsApplications of AI.- Ethics and Smart City.- Green and Sustainable AI.- Machine Learning.- Natural Language Processing.- Robotics and Computer Vision.- Simulation and Forecasting.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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With emphasis on stochastic aspects of deterministic systems this short book introduces the reader to the basic facts and some special topics of applied ergodic theory. It adresses advanced undergraduate and graduate students from various disciplines, i.e. mathematicians, physicists, electrical and mechanical engineers. Based upon a sound (but non-technical) mathematical introduction, a number of typical examples from applications (mostly from mechanics) are thoroughly discussed. By studying both probabilistic and deterministic features of dynamical systems the reader will develop what might be considered a unified view on chaos and chance as two sides of the same thing.

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Book SynopsisThis volume presents recent developments in the area of Lévy-type processes and more general stochastic processes that behave locally like a Lévy process. Although written in a survey style, quite a few results are extensions of known theorems, and others are completely new. The focus is on the symbol of a Lévy-type process: a non-random function which is a counterpart of the characteristic exponent of a Lévy process. The class of stochastic processes which can be associated with a symbol is characterized, various schemes constructing a stochastic process from a given symbol are discussed, and it is shown how one can use the symbol in order to describe the sample path properties of the underlying process. Lastly, the symbol is used to approximate and simulate Levy-type processes.This is the third volume in a subseries of the Lecture Notes in Mathematics called Lévy Matters. Each volume describes a number of important topics in the theory or applications of Lévy processes and pays tribute to the state of the art of this rapidly evolving subject with special emphasis on the non-Brownian world.Table of ContentsA Primer on Feller Semigroups and Feller Processes.- Feller Generators and Symbols.- Construction of Feller Processes.- Transformations of Feller Processes.- Sample Path Properties.- Global Properties.- Approximation.- Open Problems.- References.- Index.

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Book SynopsisIn these lecture notes, we will analyze the behavior of random walk on disordered media by means of both probabilistic and analytic methods, and will study the scaling limits. We will focus on the discrete potential theory and how the theory is effectively used in the analysis of disordered media. The first few chapters of the notes can be used as an introduction to discrete potential theory.Recently, there has been significant progress on the theory of random walk on disordered media such as fractals and random media. Random walk on a percolation cluster(‘the ant in the labyrinth’)is one of the typical examples. In 1986, H. Kesten showed the anomalous behavior of a random walk on a percolation cluster at critical probability. Partly motivated by this work, analysis and diffusion processes on fractals have been developed since the late eighties. As a result, various new methods have been produced to estimate heat kernels on disordered media. These developments are summarized in the notes.Table of ContentsIntroduction.- Weighted graphs and the associated Markov chains.- Heat kernel estimates – General theory.- Heat kernel estimates using effective resistance.- Heat kernel estimates for random weighted graphs.- Alexander-Orbach conjecture holds when two-point functions behave nicely.- Further results for random walk on IIC.- Random conductance model.

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Book SynopsisThis text provides a framework in which the main objectives of the field of uncertainty quantification (UQ) are defined and an overview of the range of mathematical methods by which they can be achieved. Complete with exercises throughout, the book will equip readers with both theoretical understanding and practical experience of the key mathematical and algorithmic tools underlying the treatment of uncertainty in modern applied mathematics. Students and readers alike are encouraged to apply the mathematical methods discussed in this book to their own favorite problems to understand their strengths and weaknesses, also making the text suitable for a self-study.Uncertainty quantification is a topic of increasing practical importance at the intersection of applied mathematics, statistics, computation and numerous application areas in science and engineering. This text is designed as an introduction to UQ for senior undergraduate and graduate students with a mathematical or statistical background and also for researchers from the mathematical sciences or from applications areas who are interested in the field.T. J. Sullivan was Warwick Zeeman Lecturer at the Mathematics Institute of the University of Warwick, United Kingdom, from 2012 to 2015. Since 2015, he is Junior Professor of Applied Mathematics at the Free University of Berlin, Germany, with specialism in Uncertainty and Risk Quantification.Trade Review“Book is one of very few that discuss a vast array of topics in the developing field of uncertainty quantification (UQ). … The text is mathematically rigorous, and though the intended audience is the senior undergraduate or early graduate mathematics student … . this is a book I might recommend to students as a reference for topics related to UQ ... . Overall, this introduction to UQ leaves something to be desired. It is well written … .” (Talea L. Mayo, SIAM Review, Vol. 59 (1), March, 2017)“This book presents a collection of mathematical results related to Uncertainly Quantification (UQ). It is intended as a textbook for senior undergraduate or graduate students with a background in mathematics and statistics. … The book might be suitable for a research seminar where students are exposed for the first time to the mathematics behind UQ.” (Elisabeth Ullmann, Mathematical Reviews, February, 2017)“This book aims to provide an introduction to the mathematics of the quantification of uncertainty. It is intended for students in mathematics and statistics. In the US this would be a graduate level textbook.” (William J. Satzer, MAA Reviews, maa.org, February, 2016)Table of ContentsIntroduction.- Measure and Probability Theory.- Banach and Hilbert Spaces.- Optimization Theory.- Measures of Information and Uncertainty.- Bayesian Inverse Problems.- Filtering and Data Assimilation.- Orthogonal Polynomials and Applications.- Numerical Integration.- Sensitivity Analysis and Model Reduction.- Spectral Expansions.- Stochastic Galerkin Methods.- Non-Intrusive Methods.- Distributional Uncertainty.- References.- Index.

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Book SynopsisThe purpose of this book is twofold: first, it sets out to equip the reader with a sound understanding of the foundations of probability theory and stochastic processes, offering step-by-step guidance from basic probability theory to advanced topics, such as stochastic differential equations, which typically are presented in textbooks that require a very strong mathematical background. Second, while leading the reader on this journey, it aims to impart the knowledge needed in order to develop algorithms that simulate realistic physical systems. Connections with several fields of pure and applied physics, from quantum mechanics to econophysics, are provided. Furthermore, the inclusion of fully solved exercises will enable the reader to learn quickly and to explore topics not covered in the main text. The book will appeal especially to graduate students wishing to learn how to simulate physical systems and to deepen their knowledge of the mathematical framework, which has very deep connections with modern quantum field theory.Table of Contents1 Review of Probability Theory.- 2 Applications to Mathematical Statistics.- 3 Conditional Probability and Conditional Expectation.- 4 Markov Chains.- 5 Sampling of Random Variables and Simulation.- 6 Brownian Motion.- 7 Introduction to Stochastic Calculus and Ito Integral.- 8 Introduction to Stochastic Differential Equations and Applications.- Bibliography.- Solutions.

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Book SynopsisThis book covers the classical theory of Markov chains on general state-spaces as well as many recent developments. The theoretical results are illustrated by simple examples, many of which are taken from Markov Chain Monte Carlo methods. The book is self-contained, while all the results are carefully and concisely proven. Bibliographical notes are added at the end of each chapter to provide an overview of the literature. Part I lays the foundations of the theory of Markov chain on general states-space. Part II covers the basic theory of irreducible Markov chains on general states-space, relying heavily on regeneration techniques. These two parts can serve as a text on general state-space applied Markov chain theory. Although the choice of topics is quite different from what is usually covered, where most of the emphasis is put on countable state space, a graduate student should be able to read almost all these developments without any mathematical background deeper than that needed to study countable state space (very little measure theory is required). Part III covers advanced topics on the theory of irreducible Markov chains. The emphasis is on geometric and subgeometric convergence rates and also on computable bounds. Some results appeared for a first time in a book and others are original. Part IV are selected topics on Markov chains, covering mostly hot recent developments.Table of ContentsPart I Foundations.- Markov Chains: Basic Definitions.- Examples of Markov Chains.- Stopping Times and the Strong Markov Property.- Martingales, Harmonic Functions and Polsson-Dirichlet Problems.- Ergodic Theory for Markov Chains.- Part II Irreducible Chains: Basics.- Atomic Chains.- Markov Chains on a Discrete State Space.- Convergence of Atomic Markov Chains.- Small Sets, Irreducibility and Aperiodicity.- Transience, Recurrence and Harris Recurrence.- Splitting Construction and Invariant Measures.- Feller and T-kernels.- Part III Irreducible Chains: Advanced Topics.- Rates of Convergence for Atomic Markov Chains.- Geometric Recurrence and Regularity.- Geometric Rates of Convergence.- (f, r)-recurrence and Regularity.- Subgeometric Rates of Convergence.- Uniform and V-geometric Ergodicity by Operator Methods.- Coupling for Irreducible Kernels.- Part IV Selected Topics.- Convergence in the Wasserstein Distance.- Central Limit Theorems.- Spectral Theory.- Concentration Inequalities.- Appendices.- A Notations.- B Topology, Measure, and Probability.- C Weak Convergence.- D Total and V-total Variation Distances.- E Martingales.- F Mixing Coefficients.- G Solutions to Selected Exercises.

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