Probability and statistics Books

Book SynopsisInterpreting statistical data as evidence, Statistical Evidence: A Likelihood Paradigm focuses on the law of likelihood, fundamental to solving many of the problems associated with interpreting data in this way. Statistics has long neglected this principle, resulting in a seriously defective methodology. This book redresses the balance, explaining why science has clung to a defective methodology despite its well-known defects. After examining the strengths and weaknesses of the work of Neyman and Pearson and the Fisher paradigm, the author proposes an alternative paradigm which provides, in the law of likelihood, the explicit concept of evidence missing from the other paradigms. At the same time, this new paradigm retains the elements of objective measurement and control of the frequency of misleading results, features which made the old paradigms so important to science. The likelihood paradigm leads to statistical methods that have a compelling rationale and an elegant simplicity, noTrade Review"...provides the explicit concept of evidence missing from the other approaches."-Aslib Book Guide "…the book is well written and readable."--Hoben Thomas, Journal of Mathematical Psychology"This (hardback) book provides a very readable discussion of a possible alternative to both the Neyman-Pearson and the Fisherian approaches to the problem of interpreting data as evidence…present this area of work in a accessible manner with a clear readable style. The main ideas are made easy to understand and well illustrated with some interesting examples, including in an appendix the paradox of the ravens. Diagrams and tables are well used in this respect and the number of formulae is kept low, which aids readability…provides a well-presented discussion of an interesting new way of looking at data which would be accessible to most with some understanding of statistics. For this reason I would recommend it to a library."--Thomas Chadwick, University of Newcastle, BiometricsTable of ContentsStatistical Evidence: A Likelihood Paradigm

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Book SynopsisThis book covers the main models within the GLM (i.e., logistic, Poisson, negative binomial, ordinal, and multinomial). For each model, estimations, interpretations, model fit, diagnostics, and how to convey results graphically are provided. There is a focus on graphic displays of results as these are a core strength of using R for statistical analysis. Many in the social sciences are transitioning away from using Stata, SPSS and SAS, to using R, and this book uses statistical models which are relevant to the social sciences. Social Science Applications of Regression for Categorical Outcomes Using R will be useful for graduate students in the social sciences who are looking to expand their statistical knowledge, and for Quantitative social scientists due to it's ability to act as a practitioners guide. Key Features: Applied- in the sense that we will provide code that others can easily adapt Flexible- R is basically just a fancy Table of Contents1. Introduction 2. Introduction to R Studio and Packages 3. Overview of OLS Regression and Introduction to the General Linear Model 4. Describing Categorical Variables and Some Useful Tests of Association 5. Regression for Binary Outcomes 6. Regression for Binary Outcomes – Moderation and Squared Terms 7. Regression for Ordinal Outcomes 8. Regression for Nominal Outcomes 9. Regression for Count Outcomes 10. Additional Outcome Types 11. Special Topics: Comparing Between Models and Missing Data

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Book SynopsisHow to Use SPSS is designed with the novice computer user in mind and for people who have no previous experience using SPSS. Each chapter is divided into short sections that describe the statistic being used, important underlying assumptions, and how to interpret the results and express them in a research report.The book begins with the basics, such as starting SPSS, defining variables, and entering and saving data. It covers all major statistical techniques typically taught in beginning statistics classes, such a descriptive statistics, graphing data, prediction and association, parametric inferential statistics, nonparametric inferential statistics and statistics for test construction.More than 275 screenshots (including sample output) throughout the book show students exactly what to expect as they follow along using SPSS. The book includes a glossary of statistical terms and practice exercises. A complete set of online resources including video tutorials and output files for students, and PowerPoint slides and test bank questions for instructors, make How to Use SPSS the definitive, field-tested resource for learning SPSS.New to this edition: Fully updated to the reflect SPSS version 29. Every screen shot has been recaptured. New video supplements for all practice exercises. References to significance levels have been updated to reflect the new SPSS output format. Effect size is now shown in output for many procedures and reference to some effect size has been moved from Appendix A to be more integrated into the chapters. Sample results sections now also include effect size where SPSS directly calculates effect size. A new section covering the EXPLORE command has been added to Chapter 3. Table of ContentsPreface to the Twelfth Edition 1. Getting Started 2. Entering and Modifying Data 3. Descriptive Statistics 4. Graphing Data 5. Prediction and Association 6. Basic Parametric Inferential Statistics and t-tests 7. ANOVA Models 8. Nonparametric Inferential Statistics 9. Test Construction Appendix A. Effect Size Appendix B. Practice Exercise Data Sets Appendix C. Sample Data Files Used in Text Appendix D. SPSS Syntax Basics Appendix E. Glossary Appendix F. Selecting the Appropriate Inferential Test Appendix G. Answer Key

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Book SynopsisThose who work in allied health professions and education aim to make people's lives better. Often, however, it is hard to know how effective this work has been: would change have occurred if there was no intervention? Is it possible we are doing more harm than good? To answer these questions and develop a body of knowledge about what works, we need to evaluate interventions. Objective intervention research is vital to improve outcomes, but this is a complex area, where it is all too easy to misinterpret evidence. This book uses practical examples to increase awareness of the numerous sources of bias that can lead to mistaken conclusions when evaluating interventions. The focus is on quantitative research methods, and exploration of the reasons why those both receiving and implementing intervention behave in the ways they do. Evaluating What Works: Intuitive Guide to Intervention Research for Practitioners illustrates how different research designs can overcome these issuesTable of Contents1. Introduction 2. Why observational studies can be misleading 3. How to select an outcome measure 4. Improvement due to nonspecific effects of intervention 5. Limitations of the pre-post design: biases related to systematic change 6. Estimating unwanted effects with a control group 7. Controlling for selection bias: randomized assignment to intervention 8. The researcher as a source of bias 9. Further potential for bias: volunteers, dropouts, and missing data 10. The randomized controlled trial as a method for controlling biases 11. The importance of variation 12. Analysis of a two-group RCT 13. How big a sample do I need? Statistical power and type II errors 14. False positives, p-hacking and multiple comparisons 15. Drawbacks of the two-arm RCT 16. Moderators and mediators of intervention effects 17. Adaptive Designs 18. Cluster Randomized Controlled Trials 19. Cross-over designs 20. Single case designs 21. Can you trust the published literature? 22. Pre-registration and Registered Reports 23. Reviewing the literature before you start 24. Putting it all together 25. Comments on exercises 26. References

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Book SynopsisFull of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis.The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the principles, indicators, and ways of representing and visualising objects that are common to the exploratory methods. The authors show how to use categorical variables in a PCA context in which variables are quantitative, how to handle more than two categorical variables in a CA context in which there are originally two variables, and how to add quantitative variablesTrade Review"While the book has some of the clearest geometric explanations written on the topic, in terms of inertia possessed by clouds of individuals and variables, its primary function is to operate as a step-by-step walk through on how to visualize, analyze and portray the results of analyses in R. This is accomplished via thought-provoking examples, ranging from wine ratings, decathlons to high-dimensional text-mining and genomic breeding. Data and code are available online, enabling fast cut-and-paste implementation…the book makes an excellent self-tutorial or teaching aid for the whole gamut of students and researchers working in applied fields. The authors are to be congratulated for their contribution to making the implementation of complex analyses ideas simple and implementable in practice."—Donna Ankherst, in Biometrics, September 2018"In the days of "big data" every researcher should be able to summarize and explain multivariate data sets. The purpose of "Exploratory Multivariate Analysis by Example using R" is to provide the practitioner with a sound understanding of, and the tools to apply, an array of multivariate technique (including Principal Components, Correspondence Analysis, and Clustering). The focus is on descriptive techniques, whose purpose is to explore the data from different perspectives, trying to find patterns, but without going into the realm of inferential statistics, with its formal tests of hypotheses, confidence intervals and other more advanced topics. This seems to be the right choice for the audience of non-statisticians to whom the book is directed. The second edition of the book includes a more extensive treatment of missing data and a new chapter on multivariate data visualization - both of which I consider very welcome additions.In summary, I consider "Exploratory Multivariate Analysis by Example using R" to be a good introduction, with an applied slant, to the fundamental multivariate techniTable of ContentsPrefacePrincipal Component Analysis (PCA)Correspondence Analysis (CA)Multiple Correspondence Analysis (MCA)ClusteringVisualisationAppendix

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Book SynopsisTopological Methods for Differential Equations and Inclusions covers the important topics involving topological methods in the theory of systems of differential equations. The equivalence between a control system and the corresponding differential inclusion is the central idea used to prove existence theorems in optimal control theory. Since the dynamics of economic, social, and biological systems are multi-valued, differential inclusions serve as natural models in macro systems with hysteresis. Table of ContentsIntroduction. 1 Background in Multi-valued Analysis. 2 Hausdor□-Pompeiu Metric Topology. 3 Measurable Multifunctions. Measurable selection. 4 Continuous Selection Theorems. 5 Linear Multivalued Operators. 6 Fixed Point Theorems. 7 Generalized Metric and Banach Spaces. 8 Fixed Point Theorems in Vector Metric and Banach Spaces. 9 Random □xed point theorem. 10 Semigroups. 11 Systems of Impulsive Di□erential Equations on the Half-line. 12 Di□erential Inclusions. 13 Random Systems of Di□erential Equations. 14 Random Fractional Di□erential Equations via Hadamard Fractional Derivatives. 15 Existence Theory for Systems of Discrete Equations. 16 Discrete Inclusions. 17 Semilinear System of Discrete Equations. 18 Discrete Boundary Value Problems. 19 Appendix.

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Book SynopsisAfter the great expansion of genome-wide association studies, their scientific methodology and, notably, their data analysis has matured in recent years, and they are a keystone in large epidemiological studies. Newcomers to the field are confronted with a wealth of data, resources and methods. This book presents current methods to perform informative analyses using real and illustrative data with established bioinformatics tools and guides the reader through the use of publicly available data. Includes clear, readable programming codes for readers to reproduce and adapt to their own data. Emphasises extracting biologically meaningful associations between traits of interest and genomic, transcriptomic and epigenomic data Uses up-to-date methods to exploit omic data Presents methods through specific examples and computing sessions Supplemented by a websTrade Review"This book is a good tool for self-learning analytical strategies for omics data. It requires previous knowledge of R and focuses on getting things done...I think the book would be a good reference for masters or PhD students that have to perform their analysis and need a starting point. Also, for the practicing statistician working with omics data."- Victor Moreno, ISCB News, July 2020 Table of Contents1 Introduction 2 Case examples 3 Dealing with omic data in Bioconductor 4 Genetic association studies 5 Genomic variant studies 6 Adressing batch effects 7 Transcriptomic studies 8 Epigenomic studies 9 Exposomic analysis 10 Enrichment analysis 11 Multiomic data analysis

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Book SynopsisThis book provides in-depth and wide-ranging analyses of the emergence, and subsequent ubiquity, of algorithms in diverse realms of social life. The plurality of Algorithmic Cultures emphasizes: 1) algorithmsâ increasing importance in the formation of new epistemic and organizational paradigms; and 2) the multifaceted analyses of algorithms across an increasing number of research fields. The authors in this volume address the complex interrelations between social groups and algorithms in the construction of meaning and social interaction. The contributors highlight the performative dimensions of algorithms by exposing the dynamic processes through which algorithms â themselves the product of a specific approach to the world â frame reality, while at the same time organizing how people think about society. With contributions from leading experts from Media Studies, Social Studies of Science and Technology, Cultural and Media Sociology from Canada, France, Germany, UK and the USA, thiTable of Contents1. What Are Algorithmic Cultures? (Jonathan Roberge / Robert Seyfert) 2. The Algorithmic Choreography of the Impressionable Subject (Lucas D. Introna3. #Trendingistrending: When Algorithms Become Culture (Tarleton Gillespie)4. Shaping Consumers’ Online Voices: Algorithmic Apparatus or Evaluation Culture? (Jean-Samuel Beuscart / Kevin Mellet)5. Deconstructing the Algorithm: Four Types of Digital Information Calculations, (Dominique Cardon)6. Baffled by an Algorithm: Mediation and the Auditory Relations of ‘Immersive Audio’ (Joe Klett)7. Algorhythmic Ecosystems: Neoliberal Couplings and Their Pathogenesis 1960–Present (Shintaro Miyazaki)8. Drones: The Mobilization of Algorithms, (Valentin Rauer)9. Social Bots as Algorithmic Pirates and Messengers of Techno-Environmental Agency, (Oliver Leistert)

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Book SynopsisProbability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors' research.This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate frTrade Review"The book can be used by upper undergraduate and graduate students as well as researchers and practitioners in statistics and data science from all disciplines…A background of calculus is required for the reader but no experience in programming is needed. The writing style of the book is extremely reader friendly. It provides numerous illustrative examples, valuable resources, a rich collection of materials, and a memorable learning experience."~Technometrics"Over many years, I have wondered about the following: Should a first undergraduate course in statistics be a Bayesian course? After reading this book, I have come to the conclusion that the answer is…yes!... this is very well written textbook that can also be used as self-learning material for practitioners. It presents a clear, accessible, and entertaining account of the interplay of probability, computations, and statistical inference from the Bayesian perspective."~ISCB NewsTable of Contents1. Introduction, examples and review. 2. Why Bayes? 3. One-parameter models. 4. Monte Carlo approximation. 5. Normal models. 6. Gibbs sampler. 7. Metropolis-Hastings algorithms, BUGS. 8. Bayesian hierarchical modeling. 9. Multivariate normal models. 10. Bayesian linear regression. 11. Bayesian model comparison, variable selection and model selection. 12. Applications.

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Book SynopsisA First Course in Fuzzy Logic, Fourth Edition is an expanded version of the successful third edition. It provides a comprehensive introduction to the theory and applications of fuzzy logic.This popular text offers a firm mathematical basis for the calculus of fuzzy concepts necessary for designing intelligent systems and a solid background for readers to pursue further studies and real-world applications.New in the Fourth Edition: Features new results on fuzzy sets of type-2 Provides more information on copulas for modeling dependence structures Includes quantum probability for uncertainty modeling in social sciences, especially in economics With its comprehensive updates, this new edition presents all the background necessary for students, instructors and professionals to begin using fuzzy logic in its manyapplications in computer science, mathemaTable of ContentsThe Concept of FuzzinessExamples. Mathematical modeling. Some operations on fuzzy sets. Fuzziness as uncertainty.Some Algebra of Fuzzy SetsBoolean algebras and lattices. Equivalence relations and partitions. Composing mappings. Isomorphisms and homomorphisms. Alpha-cuts. Images of alpha-level sets.Fuzzy QuantitiesFuzzy quantities. Fuzzy numbers. Fuzzy intervals. Logical Aspects of Fuzzy SetsClassical two-valued logic. A three-valued logic. Fuzzy logic. Fuzzy and Lukasiewicz logics. Interval-valued fuzzy logic.Basic Connectivest-norms. Generators of t-norms. Isomorphisms of t-norms. Negations. Nilpotent t-norms and negations. T-conforms. De Morgan systems. Groups and t-norms. Interval-valued fuzzy sets. Type-2 fuzzy sets.Additional Topics on ConnectivesFuzzy implications. Averaging operators. Powers of t-norms. Sensitivity of connectives. Copulas and t-norms.Fuzzy RelationsDefinitions and examples. Binary fuzzy relations. Operations on fuzzy relations. Fuzzy partitions. Fuzzy relations as Chu spaces. Approximate reasoning. Approximate reasoning in expert systems. A simple form of generalized modus ponens. The compositional rule of inference.Universal Approximation Fuzzy rule bases. Design methodologies. Some mathematical background. Approximation capability. Possibility TheoryProbability and uncertainty. Random sets. Possibility measures. Partial KnowledgeMotivations. Belief functions and incidence algebras. Monotonicity. Beliefs, densities, and allocations. Belief functions on infinite sets. Mobius transforms of set-functions. Reasoning with belief functions. Decision making using belief functions. Rough sets. Conditional events.Fuzzy MeasuresMotivation and definitions. Fuzzy measures and lower probabilities. Fuzzy measures in other areas. Conditional fuzzy measures.The Choquet IntegralThe Lebesgue integral. The Sugeno integral. The Choquet integral. Fuzzy Modeling and ControlMotivation for fuzzy control. The methodology of fuzzy control. Optimal fuzzy control. An analysis of fuzzy control techniques.

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Book SynopsisIn order to make informed decisions, there are three important elements: intuition, trust, and analytics. Intuition is based on experiential learning and recent research has shown that those who rely on their gut feelings may do better than those who don't. Analytics, however, are important in a data-driven environment to also inform decision making. The third element, trust, is critical for knowledge sharing to take place. These three elementsintuition, analytics, and trustmake a perfect combination for decision making. This book gathers leading researchers who explore the role of these three elements in the process of decision-making.Table of ContentsIntuition. The Underpinnings of Intuition. How Intuition Affects Decision Making. Data, Insights, Models, and Decisions. The Missing Link—Experiential Learning. Cases of Intuition Outperforming Analytics. Trust. The Foundation of Trust. Trust and Organizational Leadership. Trust and Knowledge Sharing. Trust and Organizational Communication. Trust and Marketing. Trust and Social Media. Analytics. The Secret Sauce. Predictive Analytics. Prescriptive Analytics. Developing an Analytics Strategy. Looking Toward the Future with Cognitive Computing and AI.

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Book SynopsisFeature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specifTable of Contents1. Preliminaries and Overview 2. Feature Engineering for Text Data 3. Feature Extraction and Learning for Visual Data 4. Feature-based time-series analysis 5. Feature Engineering for Data Streams 6. Feature Generation and Feature Engineering for Sequences 7. Feature Generation for Graphs and Networks 8. Feature Selection and Evaluation 9. Automating Feature Engineering in Supervised Learning 10. Pattern based Feature Generation 11. Deep Learning for Feature Representation 12. Feature Engineering for Social Bot Detection 13. Feature Generation and Engineering for Software Analytics 14. Feature Engineering for Twitter-based Applications

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Book SynopsisLogistic Regression Models presents an overview of the full range of logistic models, including binary, proportional, ordered, partially ordered, and unordered categorical response regression procedures. Other topics discussed include panel, survey, skewed, penalized, and exact logistic models. The text illustrates how to apply the various models to health, environmental, physical, and social science data.Examples illustrate successful modelingThe text first provides basic terminology and concepts, before explaining the foremost methods of estimation (maximum likelihood and IRLS) appropriate for logistic models. It then presents an in-depth discussion of related terminology and examines logistic regression model development and interpretation of the results. After focusing on the construction and interpretation of various interactions, the author evaluates assumptions and goodness-of-fit tests that can be used for model assessment. He also coversTrade ReviewThis book really does cover everything you ever wanted to know about logistic regression … with updates available on the author’s website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect—great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author’s goal … .—Annette J. Dobson, Biometrics, June 2012Overall this is a comprehensive book, which will provide a very useful resource and handbook for anyone whose work involves modelling binary data.—David J. Hand, International Statistical Review (2011), 79… useful as a textbook in a course on logistic regression.—Andreas Rosenblad, Technometrics, May 2011Table of ContentsPreface. Introduction. Concepts Related to the Logistic Model. Estimation Methods. Derivation of the Binary Logistic Algorithm. Model Development. Interactions. Analysis of Model Fit. Binomial Logistic Regression. Overdispersion. Ordered Logistic Regression. Multinomial Logistic Regression. Alternative Categorical Response Models. Panel Models. Other Types of Logistic-Based Models. Exact Logistic Regression. Conclusion. Appendices. References. Indices.

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Book SynopsisSurvival Analysis with Interval-Censored Data: A Practical Approach with Examples in R, SAS, and BUGS provides the reader with a practical introduction into the analysis of interval-censored survival times. Although many theoretical developments have appeared in the last fifty years, interval censoring is often ignored in practice. Many are unaware of the impact of inappropriately dealing with interval censoring. In addition, the necessary software is at times difficult to trace. This book fills in the gap between theory and practice. Features:-Provides an overview of frequentist as well as Bayesian methods.-Include a focus on practical aspects and applications.-Extensively illustrates the methods with examples using R, SAS, and BUGS. Full programs are available on a supplementary website.The authors:Kris Bogaerts is project manager at I-BioStat, KU Leuven. He received his PhD in Trade Review"The authors succeeded in providing a practical text focused on the application of interval-censored data using various statistical software. Lastly, the authors wrote a text, which appeals to practitioners, because the text anticipates their needs and the foundational concepts and software to execute it." ~ Stephanie A. Besser"All chapters spend a significant amount of time walking through examples with associated R code and results and do a very nice job explaining the initial CSE framework. Examples expand in complexity as the book progresses. As a biostatistician working in an academic setting, I am quite familiar with simulations used to construct new trials. However, the concept of CSE framework was brand new to me, and I think the strategies outlined in this book could definitely improve my approach to designing trial and analysis plans! This would also facilitate discussions with the clinical study team on how to proceed given our results. I would recommend this book to any clinical trial statistician who is interested in exploring simulations to better understand the implications of selected design and analysis strategies within their trials."~Emily Dressler, Wake Forest School of Medicine "To the best of my knowledge, this is the first book to provide a comprehensive treatment of the analysis of interval-censored data using common software such as SAS, R, and BUGS. I expect that applied statisticians and public health researchers with interest in statistical analysis of interval-censored data will find the book very useful. In addition, it seems well suited to be a reference book for a graduate-level survival analysis course. Overall, I enjoyed the presentation of the main idea of the methodology and the discussion of the strengths and limitations of approaches. If I had an opportunity to teach statistical methods for interval-censored data, I would select this book as a required text."~ Minggen Lu, The American StatisticianTable of ContentsIntroduction. Inference for Right-Censored Data. Estimation of the Survival Distribution. Comparison of Two or More Survival Distributions. Proportional Hazard Model. Accelerated Failure Time Model. Bivariate Interval-Censored Data. More Complex Problems. Other Topics in Interval Censoring.

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Book SynopsisBased on research projects supported by the National Natural Science Foundation of China and Nanjing University of Aeronautics and Astronautics, Optimization of Regional Industrial Structures and Applications provides an authoritative introduction to and survey of the cutting-edge research and applications in industrial structure optimization. Employing grey systems theory as its method of analysis, it integrates grey systems theory with industrial structure optimization theory to provide dynamic and efficient methods of measurement, analysis, and decision making. The authors cover several models of grey regional industrial structure, including grey correlation priority analysis, industrial structure order degree measurement model, regional leading industries grey assessment model and turnpike model. The first part of the book clarifies basic theory. This section covers the production and development of industrial structure theory, evolution laws of inTable of ContentsThe Forming and Development of Industrial Structure Theory. Industrial Strucutre’s Evolving Track and Law. The Key Influence Factors of Industrial Structure’s Upgrading. The Rationalization of Industrial Structure. The Heightening of Industrial Structure. The In-Output Analysis of Industrial Structure. Regional Industrial Structure. Choosing Regional Leading Industry. The Mathematics Models of Regional Industrial Structure’s Optimzing. The Research on Ma-an-Shan City’s Industrial Structure’s Optimization and Upgrading in "the 11th Five-Year Plan". The Emphasis, Ideas, and Strategy of Jiang Su Province’s Industrial Structure Adjustment. The Approach and Countermeasures of Achieving the Heightening of Industrial Structure in Jiang Su Province. The Approach and Countermeasures of the Heightening of Industrial Structure During "the 11th Five-Year Plan".

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Book SynopsisDrawing on the authors' substantial expertise in modeling longitudinal and clustered data, Quasi-Least Squares Regression provides a thorough treatment of quasi-least squares (QLS) regressiona computational approach for the estimation of correlation parameters within the framework of generalized estimating equations (GEEs). The authors present a detailed evaluation of QLS methodology, demonstrating the advantages of QLS in comparison with alternative methods. They describe how QLS can be used to extend the application of the traditional GEE approach to the analysis of unequally spaced longitudinal data, familial data, and data with multiple sources of correlation. In some settings, QLS also allows for improved analysis with an unstructured correlation matrix. Special focus is given to goodness-of-fit analysis as well as new strategies for selecting the appropriate working correlation structure for QLS and GEE. A chapter on longitudinal binary data tackTrade Review"The book does an excellent job of explaining basic concepts and techniques in the analysis of longitudinal and correlated data using QLS and GEE. Well-chosen data examples almost follow all the technical explanations, providing the readers a flavor on what problems QLS solves and how to solve those problems using software. Although the authors mainly use Stata to demonstrate the examples, they also provide web access to R, SAS, and MATLAB code and guidelines to replicate those examples, making the book appealing to a wide audience. The book also successfully incorporates some recent research work without raising its technical level. Therefore, the book will serve as a comprehensible guide to researchers who conduct analysis on correlated data. It would also be a good textbook for graduate students in statistics or biostatistics. Finally, I believe it would be a popular desk reference for methodology-oriented researchers who are interested in longitudinal studies and related fields." —Journal of the American Statistical Association, March 2015"This book deals with the quasi-least squares (QLS) regression, presenting a computational approach for the estimation of correlation parameters in the context of the generalized estimating equations (GEEs). … The book is provided with illustrative examples for each topic."—Zentralblatt MATH 1306Table of ContentsINTRODUCTION: Introduction. Review of Generalized Linear Models. QUASI-LEAST SQUARES THEORY AND APPLICATIONS: History and Theory of QLS Regression. Mixed Linear Structures and Familial Data. Correlation Structures for Clustered and Longitudinal Data. Analysis of Data with Multiple Sources of Correlation. Correlated Binary Data. Assessing Goodness of Fit and Choice of Correlation Structure for QLS and GEE. Sample Size and Demonstration. Bibliography. Index.

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Book SynopsisIn order to be effective for their users, information retrieval (IR) systems should be adapted to the specific needs of particular environments. The huge and growing array of types of information retrieval systems in use today is on display in Understanding Information Retrieval Systems: Management, Types, and Standards, which addresses over 20 types of IR systems. These various system types, in turn, present both technical and management challenges, which are also addressed in this volume. In order to be interoperable in a networked environment, IR systems must be able to use various types of technical standards, a number of which are described in this bookoften by their original developers. The book covers the full context of operational IR systems, addressing not only the systems themselves but also human user search behaviors, user-centered design, and management and policy issues. In addition to theory and practice of IR system desigTable of ContentsGeneral. Management of Information Retrieval Systems. Types of Information Retrieval Systems. Standards for Information Retrieval Systems.

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Book SynopsisExercises and Solutions in Statistical Theory helps students and scientists obtain an in-depth understanding of statistical theory by working on and reviewing solutions to interesting and challenging exercises of practical importance. Unlike similar books, this text incorporates many exercises that apply to real-world settings and provides much more thorough solutions.The exercises and selected detailed solutions cover from basic probability theory through to the theory of statistical inference. Many of the exercises deal with important, real-life scenarios in areas such as medicine, epidemiology, actuarial science, social science, engineering, physics, chemistry, biology, environmental health, and sports. Several exercises illustrate the utility of study design strategies, sampling from finite populations, maximum likelihood, asymptotic theory, latent class analysis, conditional inference, regression analysis, generalized linear models, Bayesian analysis, anTrade Review"I have found the book useful in preparing homework and exam questions in my current course, and I could see students benefiting from such a trove of problems with solutions."—The American Statistician, February 2015Table of ContentsConcepts and Notation. Basic Probability Theory. Univariate Distribution Theory. Multivariate Distribution Theory. Estimation Theory. Hypothesis Testing Theory.

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Book SynopsisA Hands-On Approach to Teaching Introductory StatisticsExpanded with over 100 more pages, Introduction to Statistical Data Analysis for the Life Sciences, Second Edition presents the right balance of data examples, statistical theory, and computing to teach introductory statistics to students in the life sciences. This popular textbook covers the mathematics underlying classical statistical analysis, the modeling aspects of statistical analysis and the biological interpretation of results, and the application of statistical software in analyzing real-world problems and datasets.New to the Second Edition A new chapter on non-linear regression models A new chapter that contains examples of complete data analyses, illustrating how a full-fledged statistical analysis is undertaken Additional exercises in most chapters A summary of statistical formulas related to the specific designs uTable of ContentsDescription of Samples and Populations. Linear Regression. Comparison of Groups. The Normal Distribution. Statistical Models, Estimation, and Confidence Intervals. Hypothesis Tests. Model Validation and Prediction. Linear Normal Models. Non-Linear Regression. Probabilities. The Binomial Distribution. Analysis of Count Data. Logistic Regression. Statistical Analysis Examples. Case Exercises. Appendices. Bibliography. Index.

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Book SynopsisWhile there have been few theoretical contributions on the Markov Chain Monte Carlo (MCMC) methods in the past decade, current understanding and application of MCMC to the solution of inference problems has increased by leaps and bounds. Incorporating changes in theory and highlighting new applications, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition presents a concise, accessible, and comprehensive introduction to the methods of this valuable simulation technique. The second edition includes access to an internet site that provides the code, written in R and WinBUGS, used in many of the previously existing and new examples and exercises. More importantly, the self-explanatory nature of the codes will enable modification of the inputs to the codes and variation on many directions will be available for further exploration.Major changes from the previous edition: · More examples with discussion of computational details in chapters on Gibbs sampling and Metropolis-Hastings algorithms · Recent developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection · Discussion of computation using both R and WinBUGS · Additional exercises and selected solutions within the text, with all data sets and software available for download from the Web · Sections on spatial models and model adequacy The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. The book will appeal to everyone working with MCMC techniques, especially research and graduate statisticians and biostatisticians, and scientists handling data and formulating models. The book has been substantially reinforced as a first reading of material on MCMC and, consequently, as a textbook for modern Bayesian computation and Bayesian inference courses.Trade Review"The new edition of the book, with its updated and additional materials, is still a great choice as at textbook for Bayesian computation and inference courses in a graduate program in computational and applied statistics. It will also be considered as one of the best textbooks for a Bayesian computational course to nonstatisticians, including social scientists and engineers." – Debajyoti Sinha, Florida State University, in JASA, March 2009“The second edition of this book is well written and builds on the first edition … The addition of an associated website is a valuable resource that contains many R scripts, allowing readers to quickly and easily test different approaches on their desired models with minimal effort. Coupling this with the depth of examples and references provided, this text provides an excellent first graduate text on MCMC methods. … The book is certainly another fine addition on the literature on MCMC and should be used by anyone interested in gaining a solid foundation in MCMC methods and algorithms. …” —Gareth Peters (University of New South Wales), Statistics in Medicine, 2008 “… one of the most comprehensive and readable texts on stochastic simulation using the technique of Markov chain Monte Carlo. … this second edition has been extensively updated to include the recent literature. New sections on spatial modeling and model adequacy have now been included, together with more illustrative material. Many of the computer codes written in R and WinBUGS … are available for download from the web. This enhances the utility of the book, both as a reference for researchers and a text on modern Bayesian computation and Bayesian inference courses for students.” —C.M. O’Brien (CEFAS Lowestoft Laboratory, UK), ISI Short Book Reviews “…The book may be quite useful as a first book on MCMC. … The treatment is nontechnical, easily read, and may be a good starting point for a statistician with little or no prior knowledge of MCMC. There is also nonstandard material. I found the material on dynamical models (including non-Gaussian ones) particularly interesting. …” —Søren Feodor Nielsen (University of Copenhagen), Journal of Applied Statistics, Vol. 34, No. 7, December 2007 “…The book does have an impressive set of exercises … it would be appropriate for a course that wants to focus on using MCMC to solve applied Bayesian inference problems.” —Galin L. Jones, Mathematical Reviews, 2007j Praise for the First Edition: “…a must for every research library, and should be given serious consideration for use as a graduate text.” —ISI Short Book Reviews “…nicely focused, elementary-level coverage…makes this book a suitable choice for an introductory course.” —Journal of the ASA, March 2000 Table of ContentsIntroduction. Bayesian Inference. Approximate Methods of Inference. Markov Chains. MCMC. Gibbs Sampling. Metropolis-Hastings Algorithms. Further Topics in MCMC.

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Book SynopsisModeled after Barbara Byrne’s other best-selling structural equation modeling (SEM) books, this practical guide reviews the basic concepts and applications of SEM using Mplus Versions 5 & 6. The author reviews SEM applications based on actual data taken from her own research. Using non-mathematical language, it is written for the novice SEM user. With each application chapter, the author "walks" the reader through all steps involved in testing the SEM model including: an explanation of the issues addressed illustrated and annotated testing of the hypothesized and post hoc models explanation and interpretation of all Mplus input and output files important caveats pertinent to the SEM application under study a description of the data and reference upon which the model was based the corresponding data and syntax files available under "Supplementary Material" below The first two chapters introduce the fundamental concepts of SEM and important basics of the Mplus program. The remaining chapters focus on SEM applications and include a variety of SEM models presented within the context of three sections: Single-group analyses, Multiple-group analyses, and other important topics, the latter of which includes the multitrait-multimethod, latent growth curve, and multilevel models.Intended for researchers, practitioners, and students who use SEM and Mplus, this book is an ideal resource for graduate level courses on SEM taught in psychology, education, business, and other social and health sciences and/or as a supplement for courses on applied statistics, multivariate statistics, intermediate or advanced statistics, and/or research design. Appropriate for those with limited exposure to SEM or Mplus, a prerequisite of basic statistics through regression analysis is recommended.Trade Review"Barbara Byrne has published another winner--a practically oriented, thorough, and accessible resource for students and researchers who want to harness the power of Mplus for their SEM analyses. The writing is clear and engaging. I anticipate assigning the book in my graduate SEM course and recommending it to fellow researchers. This book will be a valuable resource for moving from knowing about SEM to using it." - Rick H. Hoyle, Duke University, USA"This book provides a good starting point to newcomers to Mplus. It focuses, as it should for an introductory text, on the basics of 'classical' SEM. If you are new to SEM, plan on using Mplus, and are looking for an introductory text with minimal statistical jargon, this is it." - Albert Maydeu-Olivares, University of Barcelona, Spain"A solid introduction to the use of Mplus for SEM. All of the common types of structural equation models are illustrated using real examples, building the Mplus syntax from start to finish. The book is an excellent and readable guide for researchers and students who want to learn more about SEM in the context of Mplus." - Roger E. Millsap, Arizona State University, USA"A hallmark of Byrne's books is their accessibility to new users. … Byrne has done a great service to the field by bringing thousands of students and researchers to structural equation modeling through her clear writing and accessible examples. This book will be another contribution along those same lines. .... I field many, many questions … that could be answered by simply referring the asker to a book like Byrne's." - Kristopher J. Preacher, University of Kansas, USA"The book is targeted to non-mathematical readers, and hence it focuses on the applications of SEM. It does this very nicely, beginning from the part that covers the basic ideas of SEM and shows how to get started with the Mplus. Overall, this book is an excellent resource for a beginner interested in SEM with Mplus." -Kimmo Vehkalahti, Department of Social Research, Statistics, University of Helsinki, Finland"Through the use of illustrative examples, this much-needed and well-written book provides an accessible presentation of SEM with Mplus. Those new to SEM and/or Mplus will find Byrne’s book extremely useful as a companion textbook and long-term reference guide." - Sara J. Finney, James Madison University, USATable of ContentsPart 1: Introduction. 1. Structural Equation Models: The Basics. 2. Using the Mplus Program. Part 2: Single-Group Analyses. Confirmatory Factor Analytic Models 3. Testing the Factorial Validity of a Theoretical Construct (1st-order CFA Model). 4. Testing the Factorial Validity of Scores from a Measuring Instrument (1st-order CFA Model). 5. Testing the Validity of Scores from a Measuring Instrument (2nd-order CFA Model). The Full Latent Variable Model 6. Testing the Validity of a Causal Structure. Part 3: Multiple-Group Analyses. Confirmatory Factor Analytic Models 7. Testing for the Factorial Equivalence of a Measuring Instrument (Analysis of Covariance Structures). 8. Testing for the Equivalence of Latent Factor Means (Analysis of Mean and Covariance Structures). The Full Latent Variable Model 9. Testing for the Equivalence of a Causal Structure (Analysis of Covariance Structures). Part 4: Other Important Topics. 10. Testing Evidence of Construct Validity: The Multitrait-multimethod Model. 11. Testing Change Over Time: The Latent Growth Curve Model. 12. Testing Within- and Between-level Variability: The Multilevel Model.

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Book SynopsisThis is the first book to demonstrate the application of power analysis to the newer more advanced statistical techniques that are increasingly used in the social and behavioral sciences. Both basic and advanced designs are covered. Readers are shown how to apply power analysis to techniques such as hierarchical linear modeling, meta-analysis, and structural equation modeling. Each chapter opens with a review of the statistical procedure and then proceeds to derive the power functions. This is followed by examples that demonstrate how to produce power tables and charts. The book clearly shows how to calculate power by providing open code for every design and procedure in R, SAS, and SPSS. Readers can verify the power computation using the computer programs on the book's website. There is a growing requirement to include power analysis to justify sample sizes in grant proposals. Most chapters are self-standing and can be read in any order without much disruption.This book will help readers do just that. Sample computer code in R, SPSS, and SAS at www.routledge.com/9781848729810 are written to tabulate power values and produce power curves that can be included in a grant proposal.Organized according to various techniques, chapters 1 – 3 introduce the basics of statistical power and sample size issues including the historical origin, hypothesis testing, and the use of statistical power in t tests and confidence intervals. Chapters 4 - 6 cover common statistical procedures -- analysis of variance, linear regression (both simple regression and multiple regression), correlation, analysis of covariance, and multivariate analysis. Chapters 7 - 11 review the new statistical procedures -- multi-level models, meta-analysis, structural equation models, and longitudinal studies. The appendixes contain a tutorial about R and show the statistical theory of power analysis. Intended as a supplement for graduate courses on quantitative methods, multivariate statistics, hierarchical linear modeling (HLM) and/or multilevel modeling and SEM taught in psychology, education, human development, nursing, and social and life sciences, this is the first text on statistical power for advanced procedures. Researchers and practitioners in these fields also appreciate the book‘s unique coverage of the use of statistical power analysis to determine sample size in planning a study. A prerequisite of basic through multivariate statistics is assumed.Trade Review"This book extends earlier landmark texts by adding sample-size estimation for multilevel and longitudinal designs, meta-analysis, and structural-equation modeling. It is written thoughtfully and understandably. Readers will benefit enormously from the inclusion of computer code (in R, SAS and SPSS) for conducting the power analyses described. I recommend the book very highly to any researcher who wants to design research in the social sciences." - John B. Willett, Harvard University, USA"The author skillfully blends simple explanations of core concepts with more advanced material in a way that will make the work attractive to a range of readers in psychology and related disciplines. This text will be useful for postgraduate quantitative methods courses and for researchers. The coverage - from t tests through to multilevel models and SEM - is impressive. I found the examples of R, SPSS, and SAS code invaluable." - Thom Baguley, Nottingham Trent University, UK"This is a long-awaited, comprehensive book on power analysis after Cohen’s (1988) seminal book. The updated content accompanied by sample computer code is well suited for quantitative researchers in the social and behavioral sciences." - Wei Pan, Duke University, USA"This book provides a more comprehensive treatment of power analysis than any other work. ... This is likely to be the "go to" book for more complex designs. ... I found the writing style clear. ... The primary audience for this book would be all investigators who seek external funding for their work." - Warren W. Tryon, Fordham University, USA"This book would be good for departments of psychology, sociology, social work, nursing and public health. Most of the PhD programs in these departments have an advanced research methods course that could use this book. ... The Cohen book has been the standard in the field for over 20 years. ... This book would make a very nice update on a classic." - Jay Maddock, University of Hawaii, USA"This book is] a valuable extension beyond what is currently provided by other books on power. This book would contribute significantly to the field, most notably by covering the advanced and more complex techniques. …Liu and his work are well known in this field.…[This] book…could serve as the primary text for a …course on power. ...This book would basically have the field to itself." – Geoff Cumming, La Trobe University, AustraliaTable of Contents1. Introduction 2. Statistical Power 3. Power of Confidence Interval 4. Analysis of Variance 5. Linear Regression 6. Multivariate Analysis 7. Multi-level Models 8. Complex Multi-level Models 9. Meta-analysis 10. Structural Equation Models 11. Longitudinal Studies Appendix A. Cumulative Distribution Function for t, F, or x Appendix B. R Tutorial

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Book SynopsisThis classic textbook offers a clear exposition of modern probability theory and of the interplay between the properties of metric spaces and probability measures. The first half of the book gives an exposition of real analysis: basic set theory, general topology, measure theory, integration, an introduction to functional analysis in Banach and Hilbert spaces, convex sets and functions and measure on topological spaces. The second half introduces probability based on measure theory, including laws of large numbers, ergodic theorems, the central limit theorem, conditional expectations and martingale's convergence. A chapter on stochastic processes introduces Brownian motion and the Brownian bridge. The edition has been made even more self-contained than before; it now includes a foundation of the real number system and the Stone-Weierstrass theorem on uniform approximation in algebras of functions. Several other sections have been revised and improved, and the comprehensive historical nTrade Review'A marvellous work which will soon become a standard text in the field for both teaching and reference … a complete and pedagogically perfect presentation of both the necessary preparatory material of real analysis and the proofs throughout the text. Some of the topics and proofs are rarely found in other textbooks.' Proceedings of the Edinburgh Mathematical Society'Careful, scholarly, and stimulating. It would be a pleasure to teach a mathematically-oriented graduate-level course from this text.' Short Book Reviews of the ISI'[It] will serve for a long time as a standard reference.' Zentralblatt fur und ihre Grenzgebiete'What makes the book special … is the care and scholarship with which the material is treated, and the choice of additional topics … not usually covered in first year graduate courses.' Mathematical Reviews'The book serves as a clear, rigorous, and complete introduction to modern probability theory using methods of mathematical analysis, and a description of relations between the two fields … it could be very useful for students interested in learning both topics, it can also serve as complementary reading to standard lectures. Teachers preparing their graduate level courses can use the book as an excellent, rigorously written and complete source.' EMS NewsletterTable of Contents1. Foundations: set theory; 2. General topology; 3. Measures; 4. Integration; 5. Lp spaces: introduction to functional analysis; 6. Convex sets and duality of normed spaces; 7. Measure, topology, and differentiation; 8. Introduction to probability theory; 9. Convergence of laws and central limit theorems; 10. Conditional expectations and martingales; 11. Convergence of laws on separable metric spaces; 12. Stochastic processes; 13. Measurability: Borel isomorphism and analytic sets; Appendixes: A. Axiomatic set theory; B. Complex numbers, vector spaces, and Taylor's theorem with remainder; C. The problem of measure; D. Rearranging sums of nonnegative terms; E. Pathologies of compact nonmetric spaces; Indices.

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Book SynopsisThe authors believe that a proper treatment of probability theory requires an adequate background in the theory of finite measures in general spaces. The first part of their book sets out this material in a form which not only provides an introduction for intending specialists in measure theory but also meets the needs of students of probability.Table of ContentsPreface; 1. Theory of sets; 2. Point set topology; 3. Set functions; 4. Construction and propertied of measures; 5. Definitions and properties of the integral; 6. Related spaces and measures; 7. The space of measurable functions; 8. Linear functionals; 9. Structure of measures in special spaces; 10. What is probability?; 11. Random variables; 12. Characteristic functions; 13. Independence; 14. Finite collections of random variables; 15. Stochastic processes.

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Book SynopsisStructural equation modelling (SEM) is a technique that is used to estimate, analyse and test models that specify relationships among variables. The ability to conduct such analyses is essential for many problems in ecology and evolutionary biology. This book begins by explaining the theory behind the statistical methodology, including chapters on conceptual issues, the implementation of an SEM study and the history of the development of SEM. The second section provides examples of analyses on biological data including multi-group models, means models, P-technique and time-series. The final section of the book deals with computer applications and contrasts three popular SEM software packages. Aimed specifically at biological researchers and graduate students, this book will serve as valuable resource for both learning and teaching the SEM methodology. Moreover, data sets and programs that are presented in the book can also be downloaded from a website to assist the learning process.Table of ContentsPart I. Theory: 1. Structural equation modelling: an introduction Scott L. Hershberger, George A. Marcoulides and Makeba M. Parramore; 2. Concepts of structural equation modelling in biological research Bruce H. Pugesek; 3. Modelling a complex conceptual theory of population change in the Shiras moose: history and recasting as a structural equation model Bruce H. Pugesek; 4. A short history of structural equation models Adrian Tomer; 5. Guidelines for the implementation and publication of structural equation models Adrian Tomer and Bruce H. Pugesek; Part II. Applications: 6. Modelling intra-individual variability and change in bio-behavioural developmental processes Patricia H. Hawley and Todd D. Little; 7. Examining the relationship between environmental variables and ordination axes using latent variables and structural equation modelling James B. Grace; 8. From biological hypotheses to structural equation models: the imperfection of causal translation Bill Shipley; 9. Analysing dynamic systems: a comparison of structural equation modelling and system dynamics modelling Peter S. Hovmand; 10. Estimating analysis of variance models as structural equation models Michael J. Rovine and Peter C. M. Molenaar; 11. Comparing groups using structural equations James B. Grace; 12. Modelling means in latent variable models of natural selection Bruce H. Pugesek; 13. Modeling manifest variables in longitudinal designs - a two-stage approach Bret E. Fuller, Alexander von Eye; Philip K. Wood and Bobby D. Keeland; Part III. Computing: 14. A comparison of the SEM software packages Amos, EQS and LISREL Alexander von Eye and Bret E. Fuller; Index.

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