Probability and statistics Books
Oxford University Press, USA Bayesian Nets and Causality Philosophical and Computational Foundations
Book SynopsisBayesian nets are used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. This book brings together how to automate reasoning in artificial intelligence, and the nature of causality and probability in philosophy.Trade ReviewThe book will certainly be appreciated by researchers and graduate students in computer science, mathematics and philosophy and, in particular, by all interested in the complicated relations between subjective and objective interpretations of probabilistic phenomena. * EMS Newsletter *Bayesian Nets and Causality is a very well-written and well-organized book ... No doubt it will be recognized as a very important contribution to the philosophy of probability and causality by a young distinguished philosopher. * Sungho Choi, Mind *Table of ContentsIntroduction ; Probability ; Bayesian Nets ; Causal Nets: Foundational Problems ; Objective Bayesianism ; Two-Stage Bayesian Nets ; Causality ; Discovering Causal Relationships ; Epistemic Causality ; Recursive Causality ; Logic ; Language Change ; References ; Index
£115.00
Clarendon Press Poisson Processes
Book SynopsisIn the theory of random processes there are two that are fundamental, and occur over and over again, often in surprising ways. There is a real sense in which the deepest results are concerned with their interplay. One, the Bachelier Wiener model of Brownian motion, has been the subject of many books. The other, the Poisson process, seems at first sight humbler and less worthy of study in its own right. Nearly every book mentions it, but most hurry past to more general point processes or Markov chains. This comparative neglect is ill judged, and stems from a lack of perception of the real importance of the Poisson process. This distortion partly comes about from a restriction to one dimension, while the theory becomes more natural in more general context. This book attempts to redress the balance. It records Kingman''s fascination with the beauty and wide applicability of Poisson processes in one or more dimensions. The mathematical theory is powerful, and a few key results often produTrade Review'provides an enjoyable and clearly written introduction to the structure and properties of Poisson processes ... Thanks to skillful steering away from, and around, technicalities, it is widely accessible. If you don't know the story, read this book - you will then know what you are missing. If you do know it, a browse through the book, particularly the later chapters, is still worthwhile for interesting perspectives on several areas.' P.J. Donnelly, Queen Mary and Westfield College, Short Book Reviews (Publication of the International Statistical Institute)'It records the author's fascination with the beauty and wide applicability of Poisson processes in one or more dimensions.' L'Enseignement Mathématique, 3-4, 1993'Every mathematician with some knowledge of stochastic processes is aware of the interest and importance of the Poisson process. Therefore it is very useful to have now a book which is devoted to a systematic treatment of Poisson processes. The book ... fulfills the expectations one might have when a famous elder author writes a book on a classic topic. It gives the basic facts in a clear and lucid way. It is shown how the theory can be applied to interesting problems of astronomy, queueing and traffic, etc., and these examples are studied very thoroughly and deeply, giving even the specialist new insights ... an excellent basis for lectures or seminars .... a valuable gift for a young mathematician to stimulate his or her interest in stochastic processes and in applied probability in general.' Mathematical Reviews, Issue 94a'The presentation everywhere is rigorous without being fuzzy about measure theoretical details; this would make the monograph suitable for many readrs, who are either not interested or not trained in measure theoretical subtleties ... a useful addition to the literature both for various beginners as well as for lecturers in the theory of stochastic processes who would find in it a rich array of topics presented clearly.' S.D. Chatterji, Mathematics Abstracts, 773/93The Poisson process is surely the most beautiful object in probability theory, and John Kingman is its most gifted expositor. One might have been forgiven for thinking that there would be little new to say, but in fact this book is studded with new and fascinating insights. It is rare to find a book that simultaneously addresses the beginner and the expert. If there were a prize for the wisest probability book of the decade it would have to go to Bristol's Vice-Chancellor. * David Kendall, Cambridge, Journal of Royal Statistical Society, 1994 *Table of ContentsStochastic models for random sets of points; Poisson processes in general spaces; Sums over Poisson processes; Poisson processes on the line; Marked Poisson processes; Cox processes; Stochastic geometry; Complete random measures; The Poisson-Dirichlet distribution.
£86.45
Oxford University Press The Statistical Evaluation of Medical Tests for Classification and Prediction
Book SynopsisThis book describes statistical techniques for the design and evaluation of research studies on medical diagnostic tests, screening tests, biomarkers and new technologies for classification and prediction in medicine.Trade ReviewStatistical results are given a methodical treatment. In each chapter statistical results are motivated, stated and usually proven, and then illustrated on a variety of datasets based on actual trials. Many of the results were developed by Pepe and her colleagues, who have advanced the field in seminal ways. Chapters end with a concluding remarks section, a set of exercises, and proofs of more involved theoretical results, when needed. Concluding remarks sections nicely summarize results and discuss open research questions. References to key papers are given throughout. This structure allows the book to serve as both a classroom text and an excellent reference to the current literature. * Journal of Biopharmaceutical Studies *I very much recommend this book to a range of audiences, from those wanting an introduction to diagnostic test concepts and methods to active researchers in the area. This book is likely to stimulate considerable progress in development of new statistical methods for diagnostic tests, an area that relative to therapeutics has received little attention from biostatisticians. * Journal of Biopharmaceutical Studies *Table of Contents1. Introduction ; 2. Measures of Accuracy for Binary Tests ; 3. Comparing Binary Tests and Regression Analysis ; 4. The Receiver Operating Characteristic Curve ; 5. Estimating the ROC Curve ; 6. Covariate Effects on Continuous and Ordinal Tests ; 7. Incomplete Data and Imperfect Reference Tests ; 8. Study Design and Hypothesis Testing ; 9. More Topics and Conclusions ; References/Bibliography ; Index
£92.15
OUP Oxford Scientific Data Analysis
Book SynopsisDrawing on the author's extensive experience of supporting students undertaking projects, Scientific Data Analysis is a guide for any science undergraduate or beginning graduate who needs to analyse their own data, and wants a clear, step-by-step description of how to carry out their analysis in a robust, error-free way.Trade ReviewThis is an appealing introduction that would be accessible to a variety of students at the college level. Its strengths are clarity and directness with an abundance of good examples and case studies. * MAA Review *Table of ContentsPART I - UNDERSTANDING THE STATISTICS; PART II - ANALYSING EXPERIMENTAL DATA
£59.36
Oxford University Press Joy of Statistics A Treasury of Elementary Statistical Tools and Their Applications
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£39.89
Oxford University Press, USA In Defence of Objective Bayesianism
Book SynopsisHow strongly should you believe the various propositions that you can express?That is the key question facing Bayesian epistemology. Subjective Bayesians hold that it is largely (though not entirely) up to the agent as to which degrees of belief to adopt. Objective Bayesians, on the other hand, maintain that appropriate degrees of belief are largely (though not entirely) determined by the agent''s evidence. This book states and defends a version of objective Bayesian epistemology. According to this version, objective Bayesianism is characterized by three norms: Probability - degrees of belief should be probabilities Calibration - they should be calibrated with evidence Equivocation - they should otherwise equivocate between basic outcomesObjective Bayesianism has been challenged on a number of different fronts. For example, some claim it is poorly motivated, or fails to handle qualitative evidence, or yields counter-intuitive degrees of belief after updating, or suffers from a failureTable of ContentsPreface ; 1. Introduction ; 2. Objective Bayesianism ; 3. Motivation ; 4. Updating ; 5. Predicate Languages ; 6. Objective Bayesian Nets ; 7. Probabilistic Logic ; 8. Judgement Aggregation ; 9. Languages and Relativity ; 10. Objective Bayesianism in Perspective ; References ; Index
£92.15
Oxford University Press Introduction to State Space Time Series Analysis
Book SynopsisProviding a practical introduction to state space methods as applied to unobserved components time series models, also known as structural time series models, this book introduces time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. The only background required in order to understand the material presented in the book is a basic knowledge of classical linear regression models, of which a brief review is provided to refresh the reader''s knowledge. Also, a few sections assume familiarity with matrix algebra, however, these sections may be skipped without losing the flow of the exposition. The book offers a step by step approach to the analysis of the salient features in time series such as the trend, seasonal, and irregular components. Practical problems such as forecasting and missing values are treated in some detail. This useful book will appeal to practitioners and researchers who use time serTrade Reviewa fascinating read...excellent * CFA Society of the UK *I really recommend this book. It is a very good read and it is very reasonably priced. * Paul Eilers, The Newsletter of the Dutch Classification Society *Table of Contents1. Introduction ; 2. The Local Level Model ; 3. The Local Linear Trend Model ; 4. The Local Level Model with Seasonal ; 5. The Local Level Model with Explanatory Variable ; 6. The Local Level Model with Intervention Variable ; 7. The UK Seat Belt and Inflation Models ; 8. General Treatment of Univariate State Space Models ; 9. Multivariate Time Series Analysis ; 10. State Space and Box-Jenkins Methods for Time Series Analysis ; 11. State Space Modelling in Practice ; 12. Conclusions ; Appendix A UK Drivers KSI and Petrol Price ; Appendix B Road Traffic Fatalities in Norway and Finland ; Appendix C UK Front and Rear Seat Passengers KSI ; Appendix D UK Price Changes
£78.16
Oxford University Press Time Series Analysis by State Space Methods
Book SynopsisThis new edition updates Durbin & Koopman''s important text on the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbance terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. Additions to this second edition include the filtering of nonlinear and non-Gaussian series.Part I of the book obtains the mean and variance of the state, of a variable intended to measure the effect of an interaction and of regression coefficients, in terms of the observations.Part II extends the treatment to nonlinear and non-normal models. For these, analytical solutions are not available so methods are based on simulation.Trade ReviewReview from previous edition ...provides an up-to-date exposition and comprehensive treatment of state space models in time series analysis...This book will be helpful to graduate students and applied statisticians working in the area of econometric modelling as well as researchers in the areas of engineering, medicine and biology where state space models are used. * Journal of the Royal Statistical Society *Table of ContentsPART I: THE LINEAR STATE SPACE MODEL; PART II: NON-GAUSSIAN AND NONLINEAR STATE SPACE MODELS
£109.25
Oxford University Press An Introduction to Statistical Mechanics and Thermodynamics
Book SynopsisThis text presents the two complementary aspects of thermal physics as an integrated theory of the properties of matter. Conceptual understanding is promoted by thorough development of basic concepts. In contrast to many texts, statistical mechanics, including discussion of the required probability theory, is presented first. This provides a statistical foundation for the concept of entropy, which is central to thermal physics. A unique feature of the book is the development of entropy based on Boltzmann''s 1877 definition; this avoids contradictions or ad hoc corrections found in other texts. Detailed fundamentals provide a natural grounding for advanced topics, such as black-body radiation and quantum gases. An extensive set of problems (solutions are available for lecturers through the OUP website), many including explicit computations, advance the core content by probing essential concepts. The text is designed for a two-semester undergraduate course but can be adapted for one-semeTrade ReviewIn his innovative new text, Carnegie Mellon University physics professor Robert Swendsen presents the foundations of statistical mechanics with, as he puts it, a detour through thermodynamics. That's a desirable strategy because the statistical approach is more fundamental than the classical thermodynamics approach and has many applications to current research problems. [] The mathematical notation is carefully introduced and useful; the selected mathematical techniques are clearly explained in a conversational style that both graduate and advanced undergraduate students will find easy to follow. The author's subject organization and conceptual viewpoint address some of the shortcomings of conventional developments of thermal physics and will be helpful to students and researchers seeking a deep appreciation of statistical physics. * Physics Today *Bob Swendsen's book is very well thought out, educationally sound, and more original than other texts. * Jan Tobochnik, Kalamazoo College, USA *Robert Swendsen is a well-respected researcher who has developed many novel algorithms that illustrate his deep understanding of statistical mechanics. His textbook reflects his deep understanding and will likely have a major impact on the way statistical mechanics and thermodynamics is taught. Particularly noteworthy is Swendsen's treatment of entropy, following Boltzmann's original definition in terms of probability, and his comprehensive discussion of the fundamental principles and applications of statistical mechanics and thermodynamics. Students and instructors will enjoy reading the book as much as Swendsen obviously enjoyed writing it. * Harvey Gould, Clark University, USA *In this reader-friendly, excellent text, the author provides a unique combination of the best of two worlds: traditional thermodynamics (following Callen's footsteps) and modern statistical mechanics (including VPython codes for simulations). * Royce Zia, Virginia Polytechnic Institute and State University, USA *Swendsen is famous for developing Monte Carlo algorithms which dramatically speed up the simulation of many systems near a phase transition. The ideas for those algorithms required deep understanding of statistical mechanics, an understanding which is now fully applied to this excellent textbook. * Peter Young, University of California, USA *Table of ContentsI ENTROPY; II INTRODUCTION TO THERMODYNAMICS; III CLASSICAL STATISTICAL MECHANICS; IV QUANTUM STATISTICAL MECHANICS
£70.30
Oxford University Press STAT MODELS EPIDEMIOLOGY P
Book SynopsisThis self-contained account of the statistical basis of epidemiology has been written specifically for those with a basic training in biology, therefore no previous knowledge is assumed and the mathematics is deliberately kept at a manageable level. The authors show how all statistical analysis of data is based on probability models, and once one understands the model, analysis follows easily. In showing how to use models in epidemiology the authors have chosen to emphasize the role of likelihood, an approach to statistics which is both simple and intuitively satisfying. More complex problems can then be tackled by natural extensions of the simple methods. Based on a highly successful course, this book explains the essential statistics for all epidemiologists.Trade ReviewUnlike many textbooks in epidemiology, there is no long wordy preamble. The characteristic style is set straight away. The book is also highly successful in presenting a unified approach. What is also striking, is that the authors have managed to say something useful and clear about many of the all too numerous minor problems that are inevitably encountered in practice. In my view this is simply an excellent text. * Andrew Pickles, Institute of Psychiatry, London, Statistical Methods in Medical Research 1994:3 *An excellent text which provides the simplest and most logical exposition that I have seen of the statistical foundations for current techniques for analysing epidemiological data, and provides an excellent preparation for more detailed treatments. * Australasian Epidemiological Association News, 12/94 *Provides probably the most coherent and logical exposition of the use of statistical models in epidemiology that is currently available ... an excellent text which provides the simplest and most logical exposition that I have seen of the statistical foundations for current techniques for analysing epidemiological data, and provides an excellent preparation for more detailed treatments. * AEA News 12/94 *Clayton and Hills have filled the gap with an interesting text which is based mainly on probability models and likelihood. This is an unusual approach. but is precisely what is missing in many other textbooks for epidemiologists ... this is an important text for those interested in understanding statistical reasoning in epidemiology. * Maria Blettner, International Journal of Epidemiology *The authors have produced a text that will be extremely valuable to those teaching epidemiologic methods... Statistical Models in Epidemiology courageously cuts new paths into the traditional epidemiologic approach to statistical training. * Journal of the American Statistics Association *This book gives some very clear explanations ... Each point is well illustrated with small examples and there are exercises throughout. It is pleasing to see full solution to all the exercises. * Public Health (1994) 108 *Table of ContentsI. PROBABILITY MODELS AND LIKELIHOOD; II. REGRESSION MODELS; III. APPENDICES
£57.00
Oxford University Press Analysis of Longitudinal Data Oxford Statistical Science NCS P 25 Oxford Statistical Science Series
Book SynopsisThis second edition has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics. It contains an additional two chapters on fully parametric models for discrete repeated measures data and statistical models for time-dependent predictors.Trade ReviewThe book is readable, well-written, and amply illustrated * Technometrics, August 1995 (previous edition) *It belongs in the possession of every statistician who encouters longitudinal data. * Journal of the American Statistical Association *. . . provides an excellent bridge between novel concepts in theoretical statistics and their potential use in applied research. * Statistics in Medicine *The topics covered are too numerous to dwell on here ... If your work involves longitudinal data and you wish to update, this book will serve you very well. As a quick look-up, it is very useful. * Pharmaceutical Statistics *The authors conclude each chapter with a helpful summary or conclusion, often indicating further reading. Helpfully, they also mention the topics that they have chosen not to present, together with other recommended books for you to follow up ... They have also chosen a good selection of examples, many of them medical, with which the various methods are clearly illustrated. * Pharmaceutical Statistics *Readers with interests across a wide spectrum of application areas will find the ideas relevant and interesting ... The book is readable and well written ... It belongs to the possession of every statistician who encounters longitudinal data. * Zentralblatt MATH *Table of Contents1. Introduction ; 2. Design considerations ; 3. Exploring longitudinal data ; 4. General linear models ; 5. Parametric models for covariance structure ; 6. Analysis of variance methods ; 7. Generalized linear models for longitudinal data ; 8. Marginal models ; 9. Random effects models ; 10. Transition models ; 11. Likelihood-based methods for categorical data ; 12. Time-dependent covariates ; 13. Missing values in longitudinal data ; 14. Additional topics ; Appendix ; Bibliography ; Index
£53.20
Oxford University Press Geostatistical Reservoir Modeling
Book SynopsisPublished in 2002, the first edition of Geostatistical Reservoir Modeling brought the practice of petroleum geostatistics into a coherent framework, focusing on tools, techniques, examples, and guidance. It emphasized the interaction between geophysicists, geologists, and engineers, and was received well by professionals, academics, and both graduate and undergraduate students.In this revised second edition, Deutsch collaborates with co-author Michael Pyrcz to provide a full update on the latest tools, methods, practice, and research in the field of petroleum Geostatistics. Key geostatistical concepts such as integration of production data, scale-up, and cosimulation receive greater attention, and new topics like model checking, multiple point simulation, and production data integration are included in detail. Geostatistical methods are extensively illustrated through enhanced schematics, work flows and examples. A greater number of examples also are included, such as the integration oTable of Contents1. Introduction ; 2. Modeling Principles ; 3. Modeling Prerequisites ; 4. Modeling Methods ; 5. Model Applications ; 6. Special Topics ; Glossary and Notation ; Bibliography ; Index
£135.38
Springer Program Evaluation A Field Guide for Administrators
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£85.49
Springer Fuzzy Measure Theory
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£85.49
Springer Reliability Evaluation of Power Systems
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£142.49
Springer New Methods of Geostatistical Analysis and Graphical Presentation Distributions of Populations Over Territories
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£123.49
Springer Us Analytical Theory of Biological Populations The Springer Series on Demographic Methods and Population Analysis
Book SynopsisDrawing on his Elements of Physical Biology (1925) and most of his mathematical papers, Latka offered French readers insights into his biological thought and a concise and mathematically accessible summary of what he called recent contributions in demographic analy sis.Table of ContentsPrinciples: 1. On Evolution in Organic and Inorganic Systems. 2. On the Direction of Time. 3. On Energetics and Uncertainty. 4. Biological Stoichiometry. Demographic Analysis with Specific Application to the Human Species: 1. Introduction. 2. Relations Involving Mortality and Births. 3. Relations Involving Fertility. 4. The Progeny of a Population Element. 5. Indices and Measures of Natural Increase. 6. Relations Involving Fertility by Birth Order. 7. Relations Involving the Survival Functions of Two Individuals. 8. Extinction of a Line of Descent. 9. Conclusion. Appendix. Bibliography. Author Index. Subject Index.
£85.49
Springer Time Use Research in the Social Sciences Perspectives in Law and Psychology
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£85.49
Springer State and Local Population Projections Methodology and Analysis The Springer Series on Demographic Methods and Population Analysis
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£123.49
Springer State and Local Population Projections Methodology and Analysis The Springer Series on Demographic Methods and Population Analysis
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£123.49
£12.19
Springer Algorithmic Learning in a Random World
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£142.49
Springer Continuous Bivariate Distributions
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£125.99
Springer Modern Portfolio Optimization with NuOPT SPLUS and SBayes
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£85.49
Springer Mathematics of Financial Markets
Book SynopsisPricing by Arbitrage.- Martingale Measures.- The First Fundamental Theorem.- Complete Markets.- Discrete-time American Options.- Continuous-Time Stochastic Calculus.- Continuous-Time European Options.- The American Put Option.- Bonds and Term Structure.- Consumption-Investment Strategies.- Measures of Risk.Trade ReviewFrom the reviews: "...This book is a valuable addition to a graduate student's reference collection. The number of textbooks in mathematical finance is increasing much faster than the number of revolutionary contributions to the field, but this text stands above the crowd." SIAM Review, December 2005 From the reviews of the second edition: "The book is very carefully formatted. … this book is a valuable addition to a graduate student’s reference collection. The number of textbooks in mathematical finance is increasing much faster than the number of revolutionary contributions to the field, but this text stands above the crowd." (Alexandre D’Aspremont, SIAM Reviews, December, 2005) "The emphasis of the first edition of this book was on developing the mathematical concepts for the rapidly expanding field of mathematical finance. This second edition contains a significant number of changes and additions … . The target audience is readers with sound mathematical background on elementary concepts from measure-theoretic probability … . It should be an equally valuable resource to practitioners interested in the mathematical tools … . will be a very useful addition to any scholarly library." (Theofanis Sapatinas, Journal of Applied Sciences, Vol. 32 (6), 2005) "The second edition adds new matieral from current active research areas. A new chapter on coherent risk measures for instance reflects the recent trend in research and applications in the area of risk management. In summary, this is an excellent textbook in mathematical finance, and I can definitely recommend it." (S. Peng, Short Book Reviews of the ISI, June 2006)Table of ContentsPricing by Arbitrage * Martingale Measures * The Fundamental Theorem of Asset Pricing * Complete Markets and Martingale Representation * Stopping Times and American Options * A Review of Continuous Time Stochastic Calculus * European Options in Continuous Time * The American Option * Bonds and Term Structure * Consumption-Investment Strategies *
£89.99
Springer Queueing Theory with Applications to Packet Telecommunication
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£98.99
Springer Stochastic Finance
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£85.49
Springer Applied SemiMarkov Processes
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£104.49
Springer Stochastic Ageing and Dependence for Reliability
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£85.49
Springer The Concise Encyclopedia of Statistics Springer Reference Springer Reference
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£212.80
Springer New York Semiparametric Theory and Missing Data Springer Series in Statistics
Book SynopsisThis book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and comprehensive manner.Trade ReviewFrom the reviews: "The author, who does not need an introduction…had presented with clarity how he views three different subjects within a unified approach for statistical inference.…It is a long awaited book for a large audience of graduate students and researchers who have often found this subject matter daunting.… It is an easy decision for me to recommend this book to anyone who is interested in learning and using theories of frequentist estimation for semiparametric models and coarsened data. Even beyond his/her graduate student days, any statistical researcher interested in mastering frequentist semiparamatric estimation can pick up all the essential information from this book." (Debajyoti Sinha, American Statistical Association, JASA, March 2009, Vol. 104, No. 485) "Since much of the work in this area is very technical, it is most welcome to have a self-contained clearly written account by a highly-regarded author. The application to missing data is also clearly of great interest." R.J.A. Little for Short Book Reviews of the ISI, December 2006 "This book is focused precisely on the problem of estimation for a semiparametric model when the data are missing. This comprehensive monograph offers an in-depth look at the associated theory … . It was a great pleasure to read this masterful account of semiparametric theory for missing data problems … . It provides a valuable resource because it contains an up-to-date literature review and an exceptional account of state of the art research on the necessary theory. … I recommend it to any professional statistician." (Konstantinos Fokianos, Technometrics, Vol. 49 (2), 2007) "The book under review deals with estimation for SMs with missing, coarsened, and censored data. … The book is very clearly and informally written. The exposition is instructive and rigorous enough. There are many important examples, oriented to biomedical applications. The monograph will be useful for graduate and post-graduate students in statistics and biostatistics, as well as researchers in statistics and survival analysis." (Oleksandr Kukush, Zentralblatt MATH, Vol. 1105 (7), 2007)Table of Contentsto Semiparametric Models.- Hilbert Space for Random Vectors.- The Geometry of Influence Functions.- Semiparametric Models.- Other Examples of Semiparametric Models.- Models and Methods for Missing Data.- Missing and Coarsening at Random for Semiparametric Models.- The Nuisance Tangent Space and Its Orthogonal Complement.- Augmented Inverse Probability Weighted Complete-Case Estimators.- Improving Efficiency and Double Robustness with Coarsened Data.- Locally Efficient Estimators for Coarsened-Data Semiparametric Models.- Approximate Methods for Gaining Efficiency.- Double-Robust Estimator of the Average Causal Treatment Effect.- Multiple Imputation: A Frequentist Perspective.
£189.99
Springer New York An Introduction to Bayesian Analysis Theory and Methods Springer Texts in Statistics
Book SynopsisStatistical Preliminaries.- Bayesian Inference and Decision Theory.- Utility, Prior, and Bayesian Robustness.- Large Sample Methods.- Choice of Priors for Low-dimensional Parameters.- Hypothesis Testing and Model Selection.- Bayesian Computations.- Some Common Problems in Inference.- High-dimensional Problems.- Some Applications.Trade ReviewFrom the reviews: "This text provides a unique blend of theory, methods and applications that is suitable for a one-semester course in Bayesian analysis." C.M. O'Brien for Short Book Reviews of the ISI, December 2006 "The material of the book covers more than a one semester course and provides enough results for a second course. … the book is simultaneously useful for different readership groups. Instructors will get guidelines for preparing a course on Bayesian statistics … . Students will enjoy the excellently clear … style and the exercises at the end of each chapter. Practitioners will find plenty of classical and recent Bayesian methods. … I highly recommend the book to all readers who are interested in Bayesian statistics." (Friedrich Liese, Mathematical Reviews, Issue, 2007 g) "This book, with its 10 chapters, represents a valuable introduction to Bayesian statistics and varies among theory, methods and applications. … The book’s material is invaluable, and is presented with clarity … . Each chapter’s topics are covered by various examples and many exercises. … gives a constructive approach to the statistical analysis based on Bayes’ formula. … So, it is strongly recommended to libraries and all who are interested in statistics." (Hassan S. Bakouch, Journal of Applied Statistics, Vol. 35 (3), 2008) "Taken overall, the book should be recommended to a wide audience...as a source of interesting and mind-provoking information about Bayesian statistics. " ( ISCB News, 2008) "Bayesian analysis have arrived. … This text offers one approach based on the pedagogic decision to ‘balance theory, methods, and applications.’ … The brief introduction to classical inference … provides a nice basis for the objective Bayesian treatment offered by the authors throughout the book. … this book appealing for classically trained statisticians. … Overall, I congratulate the authors for a largely successful attempt to introduce true religion." (C. Shane Reese, Journal of the American Statistical Association, Vol. 103 (482), June, 2008) "The book under review aims to contribute to existing graduate-level introductory texts on Bayesian analysis by providing an impressive blend of theory, methods, and applications. It consists of 10 chapters and 5 appendices." (Joseph Melamed, Zentralblatt MATH, Vol. 1135 (13), 2008) "This book is an introduction to the theory and methods underlying Bayesian statistics written by three absolute experts on the field. It is primarily intended for graduate students taking a first course in Bayesian analysis or instructors preparing an introductory one-semester course on Bayesian analysis. … The book is written in a clear, relatively mathematical style … ." (Björn Bornkamp, Advances in Statistical Analysis, Issue 1, 2009) "This book introduces the mathematical theory of Bayesian analysis along the statistical line of decision theory. … This book is intended as a graduate-level analysis of mathematical problems in Bayesian statistics and can in parts be used as textbook on Bayesian theory. … Overall, if I had to recommend a good book on new advancements of Bayesian statistics in the last decade from a theoretical decision point of view, I would recommend this book." (Wolfgang Polasek, Statistical Papers, Vol. 50, 2009)Table of ContentsStatistical Preliminaries.- Bayesian Inference and Decision Theory.- Utility, Prior, and Bayesian Robustness.- Large Sample Methods.- Choice of Priors for Low-dimensional Parameters.- Hypothesis Testing and Model Selection.- Bayesian Computations.- Some Common Problems in Inference.- High-dimensional Problems.- Some Applications.
£123.49
Springer New York Inequalities Theory of Majorization and Its Applications
Book SynopsisThis greatly expanded new edition includes recent research on stochastic, multivariate and group majorization, Lorenz order, and applications in physics and chemistry, in economics and political science, in matrix inequalities, and in probability and statistics.Trade ReviewFrom the reviews of the second edition:“The second edition of the praised classic without whom I know that some people never leave home … . attempts to bring these uses to the fore so that the reader can see the extent and variation in its use. The chapters of the original version remain intact … . The bibliography has increased by over 50%. … Needless to say, the revised volume obviously continues as a celebrated classic.” (Simo Puntanen, International Statistical Review, Vol. 79 (2), 2011)Table of ContentsIntroduction.- Doubly Stochastic Matrices.- Schur-Convex Functions.- Equivalent Conditions for Majorization.- Preservation and Generation of Majorization.- Rearrangements and Majorization.- Combinatorial Analysis.- Geometric Inequalities.- Matrix Theory.- Numerical Analysis.- Stochastic Majorizations.- Probabilistic, Statistical, and Other Applications.- Additional Statistical Applications.- Orderings Extending Majorization.- Multivariate Majorization.- Convex Functions and Some Classical Inequalities.- Stochastic Ordering.- Total Positivity.- Matrix Factorizations, Compounds, Direct Products, and M-Matrices.- Extremal Representations of Matrix Functions.
£170.99
Springer New York Model Assisted Survey Sampling Springer Series in Statistics
Book SynopsisNow available in paperback, this book provides a comprehensive account of survey sampling theory and methodology suitable for students and researchers across a variety of disciplines. The first textbook that systematically extends traditional sampling theory with the aid of a modern model assisted outlook.Trade Review"I would recommend that this book be in the office of every survey methodologist."(Journal of Official Statistics)Table of ContentsI Principles of Estimation for Finite Populations and Important Sampling Designs.- 1 Survey Sampling in Theory and Practice.- 1.1 Surveys in Society.- 1.2 Skeleton Outline of a Survey.- 1.3 Probability Sampling.- 1.4 Sampling Frame.- 1.5 Area Frames and Similar Devices.- 1.6 Target Population and Frame Population.- 1.7 Survey Operations and Associated Sources of Error.- 1.8 Planning a Survey and the Need for Total Survey Design.- 1.9 Total Survey Design.- 1.10 The Role of Statistical Theory in Survey Sampling.- Exercises.- 2 Basic Ideas in Estimation from Probability Samples.- 2.1 Introduction.- 2.2 Population, Sample, and Sample Selection.- 2.3 Sampling Design.- 2.4 Inclusion Probabilities.- 2.5 The Notion of a Statistic.- 2.6 The Sample Membership Indicators.- 2.7 Estimators and Their Basic Statistical Properties.- 2.8 The ? Estimator and Its Properties.- 2.9 With-Replacement Sampling.- 2.10 The Design Effect.- 2.11 Confidence Intervals.- Exercises.- 3 Unbiased Estimation for Element Sampling Designs.- 3.1 Introduction.- 3.2 Bernoulli Sampling.- 3.3 Simple Random Sampling.- 3.3.1 Simple Random Sampling without Replacement.- 3.3.2 Simple Random Sampling with Replacement.- 3.4 Systematic Sampling.- 3.4.1 Definitions and Main Result.- 3.4.2 Controlling the Sample Size.- 3.4.3 The Efficiency of Systematic Sampling.- 3.4.4 Estimating the Variance.- 3.5 Poisson Sampling.- 3.6 Probability Proportional-to-Size Sampling.- 3.6.1 Introduction.- 3.6.2 ?ps Sampling.- 3.6.3 pps Sampling.- 3.6.4 Selection from Randomly Formed Groups.- 3.7 Stratified Sampling.- 3.7.1 Introduction.- 3.7.2 Notation, Definitions, and Estimation.- 3.7.3 Optimum Sample Allocation.- 3.7.4 Alternative Allocations under STSI Sampling.- 3.8 Sampling without Replacement versus Sampling with Replacement.- 3.8.1 Alternative Estimators for Simple Random Sampling with Replacement.- 3.8.2 The Design Effect of Simple Random Sampling with Replacement.- Exercises.- 4 Unbiased Estimation for Cluster Sampling and Sampling in Two or More Stages.- 4.1 Introduction.- 4.2 Single-Stage Cluster Sampling.- 4.2.1 Introduction.- 4.2.2 Simple Random Cluster Sampling.- 4.3 Two-Stage Sampling.- 4.3.1 Introduction.- 4.3.2 Two-Stage Element Sampling.- 4.4 Multistage Sampling.- 4.4.1 Introduction and a General Result.- 4.4.2 Three-Stage Element Sampling.- 4.5 With-Replacement Sampling of PSUs.- 4.6 Comparing Simplified Variance Estimators in Multistage Sampling.- Exercises.- 5 Introduction to More Complex Estimation Problems.- 5.1 Introduction.- 5.2 The Effect of Bias on Confidence Statements.- 5.3 Consistency and Asymptotic Unbiasedness.- 5.4 ? Estimators for Several Variables of Study.- 5.5 The Taylor Linearization Technique for Variance Estimation.- 5.6 Estimation of a Ratio.- 5.7 Estimation of a Population Mean.- 5.8 Estimation of a Domain Mean.- 5.9 Estimation of Variances and Covariances in a Finite Population.- 5.10 Estimation of Regression Coefficients.- 5.10.1 The Parameters of Interest.- 5.10.2 Estimation of the Regression Coefficients.- 5.11 Estimation of a Population Median.- 5.12 Demonstration of Result 5.10.1.- Exercises.- II Estimation through Linear Modeling, Using Auxiliary Variables.- 6 The Regression Estimator.- 6.1 Introduction.- 6.2 Auxiliary Variables.- 6.3 The Difference Estimator.- 6.4 Introducing the Regression Estimator.- 6.5 Alternative Expressions for the Regression Estimator.- 6.6 The Variance of the Regression Estimator.- 6.7 Comments on the Role of the Model.- 6.8 Optimal Coefficients for the Difference Estimator.- Exercises.- 7 Regression Estimators for Element Sampling Designs.- 7.1 Introduction.- 7.2 Preliminary Considerations.- 7.3 The Common Ratio Model and the Ratio Estimator.- 7.3.1 The Ratio Estimator under SI Sampling.- 7.3.2 The Ratio Estimator under Other Designs.- 7.3.3 Optimal Sampling Design for the ? Weighted Ratio Estimator.- 7.3.4 Alternative Ratio Models.- 7.4 The Common Mean Model.- 7.5 Models Involving Population Groups.- 7.6 The Group Mean Model and the Poststratified Estimator.- 7.7 The Group Ratio Model and the Separate Ratio Estimator.- 7.8 Simple Regression Models and Simple Regression Estimators.- 7.9 Estimators Based on Multiple Regression Models.- 7.9.1 Multiple Regression Models.- 7.9.2 Analysis of Variance Models.- 7.10 Conditional Confidence Intervals.- 7.10.1 Conditional Analysis for BE Sampling.- 7.10.2 Conditional Analysis for the Poststratification Estimator.- 7.11 Regression Estimators for Variable-Size Sampling Designs.- 7.12 A Class of Regression Estimators.- 7.13 Regression Estimation of a Ratio of Population Totals.- Exercises.- 8 Regression Estimators for Cluster Sampling and Two-Stage Sampling.- 8.1 Introduction.- 8.2 The Nature of the Auxiliary Information When Clusters of Elements Are Selected.- 8.3 Comments on Variance and Variance Estimation in Two-Stage Sampling.- 8.4 Regression Estimators Arising Out of Modeling at the Cluster Level.- 8.5 The Common Ratio Model for Cluster Totals.- 8.6 Estimation of the Population Mean When Clusters Are Sampled.- 8.7 Design Effects for Single-Stage Cluster Sampling.- 8.8 Stratified Clusters and Poststratified Clusters.- 8.9 Regression Estimators Arising Out of Modeling at the Element Level.- 8.10 Ratio Models for Elements.- 8.11 The Group Ratio Model for Elements.- 8.12 The Ratio Model Applied within a Single PSU.- Exercises.- III Further Questions in Design and Analysis of Surveys.- 9 Two-Phase Sampling.- 9.1 Introduction.- 9.2 Notation and Choice of Estimator.- 9.3 The ?* Estimator.- 9.4 Two-Phase Sampling for Stratification.- 9.5 Auxiliary Variables for Selection in Two Phases.- 9.6 Difference Estimators.- 9.7 Regression Estimators for Two-Phase Sampling.- 9.8 Stratified Bernoulli Sampling in Phase Two.- 9.9 Sampling on Two Occasions.- 9.9.1 Estimating the Current Total.- 9.9.2 Estimating the Previous Total.- 9.9.3 Estimating the Absolute Change and the Sum of the Totals.- Exercises.- 10 Estimation for Domains.- 10.1 Introduction.- 10.2 The Background for Domain Estimation.- 10.3 The Basic Estimation Methods for Domains.- 10.4 Conditioning on the Domain Sample Size.- 10.5 Regression Estimators for Domains.- 10.6 A Ratio Model for Each Domain.- 10.7 Group Models for Domains.- 10.8 Problems Arising for Small Domains; Synthetic Estimation.- 10.9 More on the Comparison of Two Domains.- Exercises.- 11 Variance Estimation.- 11.1 Introduction.- 11.2 A Simplified Variance Estimator under Sampling without Replacement.- 11.3 The Random Groups Technique.- 11.3.1 Independent Random Groups.- 11.3.2 Dependent Random Groups.- 11.4 Balanced Half-Samples.- 11.5 The Jackknife Technique.- 11.6 The Bootstrap.- 11.7 Concluding Remarks.- Exercises.- 12 Searching for Optimal Sampling Designs.- 12.1 Introduction.- 12.2 Model-Based Optimal Design for the General Regression Estimator.- 12.3 Model-Based Optimal Design for the Group Mean Model.- 12.4 Model-Based Stratified Sampling.- 12.5 Applications of Model-Based Stratification.- 12.6 Other Approaches to Efficient Stratification.- 12.7 Allocation Problems in Stratified Random Sampling.- 12.8 Allocation Problems in Two-Stage Sampling.- 12.8.1 The ? Estimator of the Population Total.- 12.8.2 Estimation of the Population Mean.- 12.9 Allocation in Two-Phase Sampling for Stratification.- 12.10 A Further Comment on Mathematical Programming.- 12.11 Sampling Design and Experimental Design.- Exercises.- 13 Further Statistical Techniques for Survey Data.- 13.1 Introduction.- 13.2 Finite Population Parameters in Multivariate Regression and Correlation Analysis.- 13.3 The Effect of Sampling Design on a Statistical Analysis.- 13.4 Variances and Estimated Variances for Complex Analyses.- 13.5 Analysis of Categorical Data for Finite Populations.- 13.5.1 Test of Homogeneity for Two Populations.- 13.5.2 Testing Homogeneity for More than Two Finite Populations.- 13.5.3 Discussion of Categorical Data Tests for Finite Populations.- 13.6 Types of Inference When a Finite Population Is Sampled.- Exercises.- IV A Broader View of Errors in Surveys.- 14 Nonsampling Errors and Extensions of Probability Sampling Theory.- 14.1 Introduction.- 14.2 Historic Notes: The Evolution ofthe Probability Sampling Approach.- 14.3 Measurable Sampling Designs.- 14.4 Some Nonprobability Sampling Methods.- 14.5 Model-Based Inference from Survey Samples.- 14.6 Imperfections in the Survey Operations.- 14.6.1 Ideal Conditions for the Probability Sampling Approach.- 14.6.2 Extension of the Probability Sampling Approach.- 14.7 Sampling Frames.- 14.7.1 Frame Imperfections.- 14.7.2 Estimation in the Presence of Frame Imperfections.- 14.7.3 Multiple Frames.- 14.7.4 Frame Construction and Maintenance.- 14.8 Measurement and Data Collection.- 14.9 Data Processing.- 14.10 Nonresponse.- Exercises.- 15 Nonresponse.- 15.1 Introduction.- 15.2 Characteristics of Nonresponse.- 15.2.1 Definition of Nonresponse.- 15.2.2 Response Sets.- 15.2.3 Lack of Unbiased Estimators.- 15.3 Measuring Nonresponse.- 15.4 Dealing with Nonresponse.- 15.4.1 Planning of the Survey.- 15.4.2 Callbacks and Follow-Ups.- 15.4.3 Subsampling of Nonrespondents.- 15.4.4 Randomized Response.- 15.5 Perspectives on Nonresponse.- 15.6 Estimation in the Presence of Unit Nonresponse.- 15.6.1 Response Modeling.- 15.6.2 A Useful Response Model.- 15.6.3 Estimators That Use Weighting Only.- 15.6.4 Estimators That Use Weighting as Well as Auxiliary Variables.- 15.7 Imputation.- Exercises.- 16 Measurement Errors.- 16.1 Introduction.- 16.2 On the Nature of Measurement Errors.- 16.3 The Simple Measurement Model.- 16.4 Decomposition of the Mean Square Error.- 16.5 The Risk of Underestimating the Total Variance.- 16.6 Repeated Measurements as a Tool in Variance Estimation.- 16.7 Measurement Models Taking Interviewer Effects into Account.- 16.8 Deterministic Assignment of Interviewers.- 16.9 Random Assignment of Interviewers to Groups.- 16.10 Interpenetrating Subsamples.- 16.11 A Measurement Model with Sample-Dependent Moments.- Exercises.- 17 Quality Declarations for Survey Data.- 17.1 Introduction.- 17.2 Policies Concerning Information on Data Quality.- 17.3 Statistics Canada’s Policy on Informing Users of Data Quality and Methodology.- Exercise.- Appendix A Principles of Notation.- Appendix B The MU284 Population.- Appendix C The Clustered MU284 Population.- Appendix D The CO124 Population.- References.- Answers to Selected Exercises.- Author Index.
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Springer A Pocket Guide to Epidemiology
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Springer-Verlag New York Inc. Analyzing Ecological Data
Book SynopsisData management and software.- Advice for teachers.- Exploration.- Linear regression.- Generalised linear modelling.- Additive and generalised additive modelling.- to mixed modelling.- Univariate tree models.- Measures of association.- Ordination First encounter.- Principal component analysis and redundancy analysis.- Correspondence analysis and canonical correspondence analysis.- to discriminant analysis.- Principal coordinate analysis and non-metric multidimensional scaling.- Time series analysis Introduction.- Common trends and sudden changes.- Analysis and modelling of lattice data.- Spatially continuous data analysis and modelling.- Univariate methods to analyse abundance of decapod larvae.- Analysing presence and absence data for flatfish distribution in the Tagus estuary, Portugal.- Crop pollination by honeybees in Argentina using additive mixed modelling.- Investigating the effects of rice farming on aquatic birds with mixed modelling.- Classification trees and radar detectioTrade ReviewFrom the reviews: "I liked the compact style of the book and really enjoyed the case studies. The book would be a suitable companion to statistics courses for both ecologists and statisticians at the introductory graduate level….All in all, I enjoyed reading the book and marvel at the wide range of sophisticated statistical models used in modern ecology."(Biometrics, 64, March 2008) "Readership: Undergraduates, postgraduates and scientists engaged in areas of the environmental sciences and ecological research. The material presented in this book has been developed and used by the authors in teaching statistics to its intended readership. The text is divided into two parts … . I have no doubt that for undergraduate students the main strength of the book will be the breadth of topics covered by the case studies – ranging from terrestrial ecology to marine biology." (C. M. O’Brien, International Statistical Review, Vol. 75 (3), 2007) "This is a practical way of analysing ecological data in which methodological approaches are combined with real data sets with the advantages and disadvantages of each strategy discussed. Who is it for? Upper undergraduates, postgraduates and researchers in ecology. Presentation It links ecological data, data analysis and discussion of the approaches. Would you recommend it? If you want an edited volume on different methods of ecological data analysis, then this book is worth looking through." (Times Higher Education, May, 2008) "The book is aimed at three types of readers: ecologists who wish to develop their own statistical skills, quantitative ecologists who want to use more advanced techniques, and statistical scientists seeking more experience analyzing ecological data. … Enjoyable aspects of the book include good graphical outputs, with interpretations, in the text. … Overall, this book is wroth the purchase price … . No other book combines as many good ecological data sets with such thoughtfully written analyses. I give this book two enthusiastic thumbs up!" (Loveday Conquest, Journal of the American Statistical Asociation, Vol. 103 (483), September, 2008) "The book aims to give readers sufficient information to apply statistical methodology in a correct and useful way. … the book meets its aim, covering a wide range of statistical techniques and dealing with many situations that are encountered in ecological statistics. This is an excellent, nicely presented and very readable book. I would highly recommend it to numerate researchers and students interested in environment and ecological data analysis." (Weiqi Luo, Journal of Applied Statistics, Vol. 36 (2), February, 2009) "Analysing Ecological Data by a group of ecologists-gone-statisticians from Scotland is the latest book in this area and based on years of teaching and consultancy experience. … The book differs from many of its competitors in its structure: it contains a general introduction to several fields of descriptive ecological data analysis (370 pages), which is augmented with 17 chapter-length case studies … . In summary, I can recommend the book primarily as advanced material for ecologists … ." (Carsten F. Dormann, Basic and Applied Ecology, Vol. (10), 2009)Table of ContentsIntroduction.- Data management and software.- Advice for teachers.- Exploration.- Linear regression.- Generalised linear modelling.- Additive and generalised additive modelling.- Introduction to mixed modelling.- Univariate tree models.- Measures of association.- Ordination--first encounter.- Principal component analysis and redundancy analysis.- Correspondence analysis and canonical correspondence analysis.- Introduction to discriminant analysis.- Principal coordinate analysis and non-metric multidimensional scaling.- Time series analysis--Introduction.- Common trends and sudden changes.- Analysis and modelling lattice data.- Spatially continuous data analysis and modelling.- Univariate methods to analyse abundance of decapod larvae.- Analysing presence and absence data for flatfish distribution in the Tagus estuary, Portugual.- Crop pollination by honeybees in an Argentinean pampas system using additive mixed modelling.- Investigating the effects of rice farming on aquatic birds with mixed modelling.- Classification trees and radar detection of birds for North Sea wind farms.- Fish stock identification through neural network analysis of parasite fauna.- Monitoring for change: using generalised least squares, nonmetric multidimensional scaling, and the Mantel test on western Montana grasslands.- Univariate and multivariate analysis applied on a Dutch sandy beach community.- Multivariate analyses of South-American zoobenthic species--spoilt for choice.- Principal component analysis applied to harbour porpoise fatty acid data.- Multivariate analysis of morphometric turtle data--size and shape.- Redundancy analysis and additive modelling applied on savanna tree data.- Canonical correspondence analysis of lowland pasture vegetation in the humid tropics of Mexico.- Estimating common trends in Portuguese fisheries landings.- Common trends in demersal communities on the Newfoundland-Labrador Shelf.- Sea level change and salt marshes in the Wadden Sea: a time series analysis.- Time series analysis of Hawaiian waterbirds.- Spatial modelling of forest community features in the Volzhsko-Kamsky reserve.
£208.99
Springer Fundamentals Of Data Mining In Genomics And Proteomics
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Springer New York Bayesian Networks and Decision Graphs Information Science and Statistics
Book SynopsisStructured in two parts, the first section focuses on probabilistic graphical models, while the second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision process and partially ordered decision problems.Trade ReviewFrom the reviews:MATHEMATICAL REVIEWS"This is indeed an invaluable text for students in information technology, engineering, and statistics. It is also very helpful for researchers in these fields and for those working in industry. The book is self-contained…The book has enough illustrative examples and exercises for the reader. All the illustrations are motivated by real applications. Moreover, the book provides a good balance between pure mathematical treatment and the applied aspects of the subject.""The Bayesian network (BN), or probabilistic expert system, is technology for automating human-life reasoning under uncertainty in specific contexts. … the book does an admirable job of concisely explaining a great range of concepts and techniques. … the book is very well written and to my knowledge nothing else meets its specific goal of quickly equipping the reader with both practical skills and sufficient theoretical background. … I certainly would not want to try to implement a BN application without reading this book.” (David Tritchler, Sankhya: Indian Journal of Statistics, Vol. 64 (B Part 3), 2002)"Professor Jensen is certainly one of the most influential researchers in the field of Bayesian networks and it is not surprising that this book represents a very clear and useful presentation of the main properties and use of graphical models. … I think that the present volume represents a useful integration of other material and a compact guide for either a student who wants an introduction to the field or a teacher who needs a reference for a course on probabilistic reasoning in AI." (Luigi Portinale, The Computer Journal, Vol. 46 (3), 2003)"This book is an introduction to Bayesian networks at an accessible level for first-year graduate or advanced undergraduate students. … I found this book to be an excellent introduction to the topic. It is well written, provides broad topic coverage, and is quite accessible to the non-expert. … I think Bayesian Networks and Decision Graphs would make a fine text for an introductory class in Bayesian networks or a useful reference for anyone interested in learning about the field." (David J. Marchette, Technometrics, Vol. 45 (2), 2003)"I can comfortably recommend this book as a primary source for topics related to Bayesian networks and decision graphs. This would be an excellent edition to any personal library." (Technometrics, Feburary 2008)From the reviews of the second edition:"The present book provides a very readable but also rigorous and comprehensive introduction to the subject. It would make a very good text for a graduate or an advanced undergraduate course. … Altogether, this is a very useful book for anyone interested in learning Bayesian networks without tears." (Jayanta K. Ghosh, International Statistical Reviews, Vol. 76 (2), 2008)"This book is the second edition of Jensen’s Bayesian Networks and Decision Graphs … . Each chapter ends with a summary section, bibliographic notes, and exercises. … provides a readable, self-contained, and above all, practical introduction to Bayesian networks and decision graphs. Its treatment is appropriate not just for statisticians, but also for computer scientists, engineers, and others researchers with appropriate mathematical background. … highly recommend it as a text or a useful reference for anyone interested in probabilistic graphical models or decision graphs." (Alyson G. Wilson, Journal of the American Statistical Association, Vol. 104 (485), March, 2009)“Devoted to Bayesian Networks or Graphical Models and Influence Diagrams, covering a full course with nice exercises … . It is useful as a reference for special topics. … strongly recommended for readers or user of BNs who are interested in specifying dependency models. … great importance to practitioners who try to find causality behind call-backs of products or crashes. … the book can be recommended to anybody working on the interface of operations research, AI, statistics and computer science.” (Hans-J. Lenz, Statistical Papers, Vol. 52, 2011)Table of ContentsCausal and Bayesian Networks * Part I: A Practical Guide to Normative Systems: Building Models * Learning, Adaptation, and Tuning * Decision Graphs * Part II: Algorithms for Normative Systems: Belief Updating in Bayesian Networks * Bayesian Network Analysis Tools * Algorithms for Influence Diagrams
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Springer Multivariate Statistics
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Springer The Bayesian Choice
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Springer Evaluating Clinical Research
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Springer Stochastic Global Optimization 9 Springer Optimization and Its Applications
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Springer Model Based Inference in the Life Sciences A Primer on Evidence
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Springer Data Manipulation with R
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Springer New York Introduction to Empirical Processes and Semiparametric Inference Springer Series in Statistics Springer Series in Statistics
Book SynopsisKosorok’s brilliant text provides a self-contained introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods.Trade ReviewFrom the reviews:"Introduction to Empirical Processes and Semiparametric Inference is a very good combination of both the empirical processes and semiparametric theories. This is the first book of its kind....I agree with the author that this book is 'more of a textbook than a research monograph.' As the semiparametric inference is currently an extremely active research area in statistical research, the book will open the door for graduate students to identify significant future research potentials. In fact, this book contains the author's newest research result, the application of semiparametric method in microarray data analysis. This book can be used as a textbook for graduate students in statistics, biostatistics, and economics (econometrics). In fact, the contents of this book can be tailored for different courses.""Generally, this is a great book on empirical processes and semiparametric methods. It should be on the must-read list for a serious statistician, biostatistician, or econometrician." (Biometrics, September 2008)"The main focus of this book is to introduce empirical processes and semiparametric inference methods to researchers interested in developing inferential tools for relatively complicated mathematical or statistical modeling problems. ...The material is structured in a sensible way supporting the learning and understanding of useful and challenging techniques of empirical processes and semiparametric inference. The book could well be very helpful for those studying and applying these techniques." (International Statistical Review 2008,77,2)“This book is an introduction to what is commonly called the modern theory of empirical processes – empirical processes indexed by classes of functions – and to semiparametric inference, and the interplay between both fields. … This is clearly intended to be a book for the novice in empirical process theory and semiparametric inference. … The main material is presented in a clearly arranged and logical order. … will be useful to anybody who wants to learn about the modern theory of empirical processes and semiparametric inference.” (Erich Häusler, Zentralblatt MATH, Vol. 1180, 2010)Table of ContentsOverview.- An Overview of Empirical Processes.- Overview of Semiparametric Inference.- Case Studies I.- Empirical Processes.- to Empirical Processes.- Preliminaries for Empirical Processes.- Stochastic Convergence.- Empirical Process Methods.- Entropy Calculations.- Bootstrapping Empirical Processes.- Additional Empirical Process Results.- The Functional Delta Method.- Z-Estimators.- M-Estimators.- Case Studies II.- Semiparametric Inference.- to Semiparametric Inference.- Preliminaries for Semiparametric Inference.- Semiparametric Models and Efficiency.- Efficient Inference for Finite-Dimensional Parameters.- Efficient Inference for Infinite-Dimensional Parameters.- Semiparametric M-Estimation.- Case Studies III.
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Springer The Science of Bradley Efron
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Springer thepleasuresofstatistics
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Springer Matrix Algebra From a Statisticians Perspective
Book SynopsisMatrices.- Submatrices and Partitioned Matrices.- Linear Dependence and Independence.- Linear Spaces: Row and Column Spaces.- Trace of a (Square) Matrix.- Geometrical Considerations.- Linear Systems: Consistency and Compatibility.- Inverse Matrices.- Generalized Inverses.- Idempotent Matrices.- Linear Systems: Solutions.- Projections and Projection Matrices.- Determinants.- Linear, Bilinear, and Quadratic Forms.- Matrix Differentiation.- Kronecker Products and the Vec and Vech Operators.- Intersections and Sums of Subspaces.- Sums (and Differences) of Matrices.- Minimization of a Second-Degree Polynomial (in n Variables) Subject to Linear Constraints.- The Moore-Penrose Inverse.- Eigenvalues and Eigenvectors.- Linear Transformations.- Erratum.Trade ReviewFrom a review: THE AUSTRALIAN AND NEW ZEALAND JOURNAL OF STATISTICS "This is a book that will be welcomed by many statisticians at most stages of professional development. ...It is essentially a carefully sequenced and tightly interlocking collections of proofs in an elementary, though very pure mathematical style." Table of ContentsPreface. - Matrices. - Submatrices and partitioned matricies. - Linear dependence and independence. - Linear spaces: row and column spaces. - Trace of a (square) matrix. - Geometrical considerations. - Linear systems: consistency and compatability. - Inverse matrices. - Generalized inverses. - Indepotent matrices. - Linear systems: solutions. - Projections and projection matrices. - Determinants. - Linear, bilinear, and quadratic forms. - Matrix differentiation. - Kronecker products and the vec and vech operators. - Intersections and sums of subspaces. - Sums (and differences) of matrices. - Minimzation of a second-degree polynomial (in n variables) subject to linear constraints. - The Moore-Penrose inverse. - Eigenvalues and Eigenvectors. - Linear transformations. - References. - Index.
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