Categorical Data Analysis


Author: Alan Agresti
Publisher: John Wiley & Sons
ISBN: 0471458767
Category: Mathematics
Page: 734
View: 4429

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Amstat News asked three review editors to rate their topfive favorite books in the September 2003 issue. CategoricalData Analysis was among those chosen. A valuable new edition of a standard reference "A 'must-have' book for anyone expecting to do research and/orapplications in categorical data analysis." –Statistics in Medicine on Categorical Data Analysis,First Edition The use of statistical methods for categorical data hasincreased dramatically, particularly for applications in thebiomedical and social sciences. Responding to new developments inthe field as well as to the needs of a new generation ofprofessionals and students, this new edition of the classicCategorical Data Analysis offers a comprehensiveintroduction to the most important methods for categorical dataanalysis. Designed for statisticians and biostatisticians as well asscientists and graduate students practicing statistics,Categorical Data Analysis, Second Edition summarizes thelatest methods for univariate and correlated multivariatecategorical responses. Readers will find a unified generalizedlinear models approach that connects logistic regression andPoisson and negative binomial regression for discrete data withnormal regression for continuous data. Adding to the value in thenew edition is coverage of: Three new chapters on methods for repeated measurement andother forms of clustered categorical data, including marginalmodels and associated generalized estimating equations (GEE)methods, and mixed models with random effects Stronger emphasis on logistic regression modeling of binaryand multicategory data An appendix showing the use of SAS for conducting nearly allanalyses in the book Prescriptions for how ordinal variables should be treateddifferently than nominal variables Discussion of exact small-sample procedures More than 100 analyses of real data sets to illustrateapplication of the methods, and more than 600 exercises An Instructor's Manual presenting detailed solutions to allthe problems in the book is available from the Wiley editorialdepartment.

An Introduction to Categorical Data Analysis


Author: Alan Agresti
Publisher: Wiley-Interscience
ISBN: 9780471113386
Category: Mathematics
Page: 312
View: 1987

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Concise, complete, nontechnical; the ideal introduction to an increasingly important topic In recent years, the use of statistical methods for categorical data has increased dramatically in a variety of areas and applications. This book provides an applied introduction to the most important methods for analyzing categorical data. It summarizes methods that have long played a prominent role, such as chi-squared tests, but places special emphasis on logistic regression and loglinear modeling techniques. Special features of the book include: Emphasis on logistic regression modeling of binary data and Poisson regression modeling of count data A unified perspective, based on generalized linear models, that connects these methods with standard regression methods for normally-distributed data An appendix showing the use of a new SAS procedure (GENMOD) for generalized linear modeling that can conduct nearly all methods presented in the book An entertaining historical perspective of the development of the methods Specialized methods for ordinal data, small samples, multicategory data, and matched pairs More than 100 examples of real data sets and more than 200 exercises Writing in an applied, nontechnical style, Alan Agresti illustrates methods using a wide variety of real data, including alcohol, cigarette, and marijuana use by teenagers; AZT use and delay of AIDS; space shuttle launches and O-ring failure; passive smoking and lung cancer; and much more. An Introduction to Categorical Data Analysis is an invaluable tool for social, behavioral, and biomedical scientists, as well as researchers in public health, marketing, education, biological and agricultural sciences, and industrial quality control. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

Analysis of Ordinal Categorical Data


Author: Alan Agresti
Publisher: John Wiley & Sons
ISBN: 1118209990
Category: Mathematics
Page: 424
View: 8316

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Statistical science’s first coordinated manual of methods for analyzing ordered categorical data, now fully revised and updated, continues to present applications and case studies in fields as diverse as sociology, public health, ecology, marketing, and pharmacy. Analysis of Ordinal Categorical Data, Second Edition provides an introduction to basic descriptive and inferential methods for categorical data, giving thorough coverage of new developments and recent methods. Special emphasis is placed on interpretation and application of methods including an integrated comparison of the available strategies for analyzing ordinal data. Practitioners of statistics in government, industry (particularly pharmaceutical), and academia will want this new edition.

Applied Categorical Data Analysis and Translational Research


Author: Chap T. Le
Publisher: John Wiley & Sons
ISBN: 0470371307
Category: Mathematics
Page: 399
View: 3780

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An updated treatment of categorical data analysis in the biomedical sciences that now explores applications to translational research Thoroughly updated with the latest advances in the field, Applied Categorical Data Analysis and Translational Research, Second Edition maintains the accessible style of its predecessor while also exploring the importance of translational research as it relates to basic scientific findings within clinical practice. With its easy–to–follow style, updated coverage of major methodologies, and broadened scope of coverage, this new edition provides an accessible guide to statistical methods involving categorical data and the steps to their application in problem solving in the biomedical sciences. Delving even further into the applied direction, this update offers many real–world examples from biomedicine, epidemiology, and public health along with detailed case studies taken straight from modern research in these fields. Additional features of the Second Edition include: A new chapter on the relationship between translational research and categorical data, focusing on design study, bioassay, and Phase I and Phase II clinical trials A new chapter on categorical data and diagnostic medicine, with coverage of the diagnostic process, prevalence surveys, the ROC function and ROC curve, and important statistical considerations A revised chapter on logistic regression models featuring an updated treatment of simple and multiple regression analysis An added section on quantal bioassays Each chapter features updated and new exercise sets along with numerous graphs that demonstrate the highly visual nature of the topic. A related Web site features the book′s examples as well as additional data sets that can be worked with using SAS® software. The only book of its kind to provide balanced coverage of methods for both categorical data and translational research, Applied Categorical Data Analysis and Translational Research, Second Edition is an excellent book for courses on applied statistics and biostatistics at the upper–undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the biomedical and public health fields.

Bayesian Models for Categorical Data


Author: Peter Congdon
Publisher: John Wiley & Sons
ISBN: 0470092386
Category: Mathematics
Page: 446
View: 9221

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The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to students and researchers alike. Emphasizing the use of statistical computing and applied data analysis, this book provides a comprehensive introduction to Bayesian methods of categorical outcomes. * Reviews recent Bayesian methodology for categorical outcomes (binary, count and multinomial data). * Considers missing data models techniques and non-standard models (ZIP and negative binomial). * Evaluates time series and spatio-temporal models for discrete data. * Features discussion of univariate and multivariate techniques. * Provides a set of downloadable worked examples with documented WinBUGS code, available from an ftp site. The author's previous 2 bestselling titles provided a comprehensive introduction to the theory and application of Bayesian models. Bayesian Models for Categorical Data continues to build upon this foundation by developing their application to categorical, or discrete data - one of the most common types of data available. The author's clear and logical approach makes the book accessible to a wide range of students and practitioners, including those dealing with categorical data in medicine, sociology, psychology and epidemiology.

Foundations of Linear and Generalized Linear Models


Author: Alan Agresti
Publisher: John Wiley & Sons
ISBN: 1118730038
Category: Mathematics
Page: 444
View: 8664

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"This book presents an overview of the foundations and the key ideas and results of linear and generalized linear models under one cover. Written by a prolific academic, researcher, and textbook writer, Foundations of Linear and Generalized Linear Modelsis soon to become the gold standard by which all existing textbooks on the topic will be compared. While the emphasis is clearly and succinctly on theoretical underpinnings, applications in "R" are presented when they help to elucidate the content or promote practical model building. Each chapter contains approximately 15-20 exercises, primarily for readers to practice and extend the theory, but, also to assimilate the ideas by doing some data analysis. The carefully crafted models and examples convey basic concepts and do not get mired down in non-trivial considerations. An author-maintained web site includes, among other numerous pedagogical supplements, analyses that parallel the "R" routines from the book in SAS, SPSS and Stata"--

Nonparametric Statistics with Applications to Science and Engineering


Author: Paul H. Kvam,Brani Vidakovic
Publisher: John Wiley & Sons
ISBN: 9780470168691
Category: Mathematics
Page: 448
View: 6745

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A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics This book presents a practical approach to nonparametric statistical analysis and provides comprehensive coverage of both established and newly developed methods. With the use of MATLAB, the authors present information on theorems and rank tests in an applied fashion, with an emphasis on modern methods in regression and curve fitting, bootstrap confidence intervals, splines, wavelets, empirical likelihood, and goodness-of-fit testing. Nonparametric Statistics with Applications to Science and Engineering begins with succinct coverage of basic results for order statistics, methods of categorical data analysis, nonparametric regression, and curve fitting methods. The authors then focus on nonparametric procedures that are becoming more relevant to engineering researchers and practitioners. The important fundamental materials needed to effectively learn and apply the discussed methods are also provided throughout the book. Complete with exercise sets, chapter reviews, and a related Web site that features downloadable MATLAB applications, this book is an essential textbook for graduate courses in engineering and the physical sciences and also serves as a valuable reference for researchers who seek a more comprehensive understanding of modern nonparametric statistical methods.

Time Series

Applications to Finance
Author: Ngai Hang Chan
Publisher: John Wiley & Sons
ISBN: 0471461644
Category: Mathematics
Page: 224
View: 7070

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Elements of Financial Time Series fills a gap in the market in thearea of financial time series analysis by giving both conceptualand practical illustrations. Examples and discussions in the laterchapters of the book make recent developments in time series moreaccessible. Examples from finance are maximized as much as possiblethroughout the book. * Full set of exercises is displayed at the end of eachchapter. * First seven chapters cover standard topics in time series at ahigh-intensity level. * Recent and timely developments in nonstandard time seriestechniques are illustrated with real finance examples indetail. * Examples are systemically illustrated with S-plus with codes anddata available on an associated Web site.

Bayesian Statistical Modelling


Author: Peter Congdon
Publisher: John Wiley & Sons
ISBN: 0470035935
Category: Mathematics
Page: 596
View: 4744

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Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets. The second edition: Provides an integrated presentation of theory, examples, applications and computer algorithms. Discusses the role of Markov Chain Monte Carlo methods in computing and estimation. Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences. Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles. Provides exercises designed to help reinforce the reader’s knowledge and a supplementary website containing data sets and relevant programs. Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students. Praise for the First Edition: “It is a remarkable achievement to have carried out such a range of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains.” – ISI - Short Book Reviews “This is an excellent introductory book on Bayesian modelling techniques and data analysis” – Biometrics “The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods.” – Journal of Mathematical Psychology

Recent Advances in Quantitative Methods in Cancer and Human Health Risk Assessment


Author: Lutz Edler,Christos Kitsos
Publisher: John Wiley & Sons
ISBN: 0470857668
Category: Mathematics
Page: 502
View: 2275

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Human health risk assessment involves the measuring of risk of exposure to disease, with a view to improving disease prevention. Mathematical, biological, statistical, and computational methods play a key role in exposure assessment, hazard assessment and identification, and dose-response modelling. Recent Advances in Quantitative Methods in Cancer and Human Health Risk Assessment is a comprehensive text that accounts for the wealth of new biological data as well as new biological, toxicological, and medical approaches adopted in risk assessment. It provides an authoritative compendium of state-of-the-art methods proposed and used, featuring contributions from eminent authors with varied experience from academia, government, and industry. Provides a comprehensive summary of currently available quantitative methods for risk assessment of both cancer and non-cancer problems. Describes the applications and the limitations of current mathematical modelling and statistical analysis methods (classical and Bayesian). Includes an extensive introduction and discussion to each chapter. Features detailed studies of risk assessments using biologically-based modelling approaches. Discusses the varying computational aspects of the methods proposed. Provides a global perspective on human health risk assessment by featuring case studies from a wide range of countries. Features an extensive bibliography with links to relevant background information within each chapter. Recent Advances in Quantitative Methods in Cancer and Human Health Risk Assessment will appeal to researchers and practitioners in public health & epidemiology, and postgraduate students alike. It will also be of interest to professionals working in risk assessment agencies.

The Theory of Measures and Integration


Author: Eric M. Vestrup
Publisher: John Wiley & Sons
ISBN: 0470317957
Category: Mathematics
Page: 624
View: 7414

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An accessible, clearly organized survey of the basic topics of measure theory for students and researchers in mathematics, statistics, and physics In order to fully understand and appreciate advanced probability, analysis, and advanced mathematical statistics, a rudimentary knowledge of measure theory and like subjects must first be obtained. The Theory of Measures and Integration illuminates the fundamental ideas of the subject-fascinating in their own right-for both students and researchers, providing a useful theoretical background as well as a solid foundation for further inquiry. Eric Vestrup's patient and measured text presents the major results of classical measure and integration theory in a clear and rigorous fashion. Besides offering the mainstream fare, the author also offers detailed discussions of extensions, the structure of Borel and Lebesgue sets, set-theoretic considerations, the Riesz representation theorem, and the Hardy-Littlewood theorem, among other topics, employing a clear presentation style that is both evenly paced and user-friendly. Chapters include: * Measurable Functions * The Lp Spaces * The Radon-Nikodym Theorem * Products of Two Measure Spaces * Arbitrary Products of Measure Spaces Sections conclude with exercises that range in difficulty between easy "finger exercises"and substantial and independent points of interest. These more difficult exercises are accompanied by detailed hints and outlines. They demonstrate optional side paths in the subject as well as alternative ways of presenting the mainstream topics. In writing his proofs and notation, Vestrup targets the person who wants all of the details shown up front. Ideal for graduate students in mathematics, statistics, and physics, as well as strong undergraduates in these disciplines and practicing researchers, The Theory of Measures and Integration proves both an able primary text for a real analysis sequence with a focus on measure theory and a helpful background text for advanced courses in probability and statistics.

Exploratory Data Mining and Data Cleaning


Author: Tamraparni Dasu,Theodore Johnson
Publisher: John Wiley & Sons
ISBN: 0471458643
Category: Mathematics
Page: 203
View: 1050

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Written for practitioners of data mining, data cleaning anddatabase management. Presents a technical treatment of data quality includingprocess, metrics, tools and algorithms. Focuses on developing an evolving modeling strategy through aniterative data exploration loop and incorporation of domainknowledge. Addresses methods of detecting, quantifying and correcting dataquality issues that can have a significant impact on findings anddecisions, using commercially available tools as well as newalgorithmic approaches. Uses case studies to illustrate applications in real lifescenarios. Highlights new approaches and methodologies, such as theDataSphere space partitioning and summary based analysistechniques. Exploratory Data Mining and Data Cleaning will serve as animportant reference for serious data analysts who need to analyzelarge amounts of unfamiliar data, managers of operations databases,and students in undergraduate or graduate level courses dealingwith large scale data analys is and data mining.

Random Graphs for Statistical Pattern Recognition


Author: David J. Marchette
Publisher: John Wiley & Sons
ISBN: 9780471722083
Category: Mathematics
Page: 264
View: 3866

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A timely convergence of two widely used disciplines Random Graphs for Statistical Pattern Recognition is the firstbook to address the topic of random graphs as it applies tostatistical pattern recognition. Both topics are of vital interestto researchers in various mathematical and statistical fields andhave never before been treated together in one book. The use ofdata random graphs in pattern recognition in clustering andclassification is discussed, and the applications for bothdisciplines are enhanced with new tools for the statistical patternrecognition community. New and interesting applications for randomgraph users are also introduced. This important addition to statistical literaturefeatures: Information that previously has been available only throughscattered journal articles Practical tools and techniques for a wide range of real-worldapplications New perspectives on the relationship between patternrecognition and computational geometry Numerous experimental problems to encourage practicalapplications With its comprehensive coverage of two timely fields, enhancedwith many references and real-world examples, Random Graphs forStatistical Pattern Recognition is a valuable resource forindustry professionals and students alike.

Multivariate Density Estimation

Theory, Practice, and Visualization
Author: David W. Scott
Publisher: John Wiley & Sons
ISBN: 1118575482
Category: Mathematics
Page: 384
View: 3380

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Clarifies modern data analysis through nonparametric density estimation for a complete working knowledge of the theory and methods Featuring a thoroughly revised presentation, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition maintains an intuitive approach to the underlying methodology and supporting theory of density estimation. Including new material and updated research in each chapter, the Second Edition presents additional clarification of theoretical opportunities, new algorithms, and up-to-date coverage of the unique challenges presented in the field of data analysis. The new edition focuses on the various density estimation techniques and methods that can be used in the field of big data. Defining optimal nonparametric estimators, the Second Edition demonstrates the density estimation tools to use when dealing with various multivariate structures in univariate, bivariate, trivariate, and quadrivariate data analysis. Continuing to illustrate the major concepts in the context of the classical histogram, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition also features: Over 150 updated figures to clarify theoretical results and to show analyses of real data sets An updated presentation of graphic visualization using computer software such as R A clear discussion of selections of important research during the past decade, including mixture estimation, robust parametric modeling algorithms, and clustering More than 130 problems to help readers reinforce the main concepts and ideas presented Boxed theorems and results allowing easy identification of crucial ideas Figures in color in the digital versions of the book A website with related data sets Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition is an ideal reference for theoretical and applied statisticians, practicing engineers, as well as readers interested in the theoretical aspects of nonparametric estimation and the application of these methods to multivariate data. The Second Edition is also useful as a textbook for introductory courses in kernel statistics, smoothing, advanced computational statistics, and general forms of statistical distributions.

Modes of Parametric Statistical Inference


Author: Seymour Geisser,Wesley O. Johnson
Publisher: John Wiley & Sons
ISBN: 0471743127
Category: Mathematics
Page: 192
View: 5057

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A fascinating investigation into the foundations of statisticalinference This publication examines the distinct philosophical foundations ofdifferent statistical modes of parametric inference. Unlike manyother texts that focus on methodology and applications, this bookfocuses on a rather unique combination of theoretical andfoundational aspects that underlie the field of statisticalinference. Readers gain a deeper understanding of the evolution andunderlying logic of each mode as well as each mode's strengths andweaknesses. The book begins with fascinating highlights from the history ofstatistical inference. Readers are given historical examples ofstatistical reasoning used to address practical problems that arosethroughout the centuries. Next, the book goes on to scrutinize fourmajor modes of statistical inference: * Frequentist * Likelihood * Fiducial * Bayesian The author provides readers with specific examples andcounterexamples of situations and datasets where the modes yieldboth similar and dissimilar results, including a violation of thelikelihood principle in which Bayesian and likelihood methodsdiffer from frequentist methods. Each example is followed by adetailed discussion of why the results may have varied from onemode to another, helping the reader to gain a greater understandingof each mode and how it works. Moreover, the author providesconsiderable mathematical detail on certain points to highlight keyaspects of theoretical development. The author's writing style and use of examples make the text clearand engaging. This book is fundamental reading for graduate-levelstudents in statistics as well as anyone with an interest in thefoundations of statistics and the principles underlying statisticalinference, including students in mathematics and the philosophy ofscience. Readers with a background in theoretical statistics willfind the text both accessible and absorbing.

The EM Algorithm and Extensions


Author: Geoffrey McLachlan,Thriyambakam Krishnan
Publisher: John Wiley & Sons
ISBN: 0470191600
Category: Mathematics
Page: 384
View: 1956

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The only single-source——now completely updated and revised——to offer a unified treatment of the theory, methodology, and applications of the EM algorithm Complete with updates that capture developments from the past decade, The EM Algorithm and Extensions, Second Edition successfully provides a basic understanding of the EM algorithm by describing its inception, implementation, and applicability in numerous statistical contexts. In conjunction with the fundamentals of the topic, the authors discuss convergence issues and computation of standard errors, and, in addition, unveil many parallels and connections between the EM algorithm and Markov chain Monte Carlo algorithms. Thorough discussions on the complexities and drawbacks that arise from the basic EM algorithm, such as slow convergence and lack of an in-built procedure to compute the covariance matrix of parameter estimates, are also presented. While the general philosophy of the First Edition has been maintained, this timely new edition has been updated, revised, and expanded to include: New chapters on Monte Carlo versions of the EM algorithm and generalizations of the EM algorithm New results on convergence, including convergence of the EM algorithm in constrained parameter spaces Expanded discussion of standard error computation methods, such as methods for categorical data and methods based on numerical differentiation Coverage of the interval EM, which locates all stationary points in a designated region of the parameter space Exploration of the EM algorithm's relationship with the Gibbs sampler and other Markov chain Monte Carlo methods Plentiful pedagogical elements—chapter introductions, lists of examples, author and subject indices, computer-drawn graphics, and a related Web site The EM Algorithm and Extensions, Second Edition serves as an excellent text for graduate-level statistics students and is also a comprehensive resource for theoreticians, practitioners, and researchers in the social and physical sciences who would like to extend their knowledge of the EM algorithm.

Methods of Multivariate Analysis


Author: Alvin C. Rencher
Publisher: John Wiley & Sons
ISBN: 0471461725
Category: Mathematics
Page: 738
View: 5401

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Amstat News asked three review editors to rate their topfive favorite books in the September 2003 issue. Methods ofMultivariate Analysis was among those chosen. When measuring several variables on a complex experimental unit,it is often necessary to analyze the variables simultaneously,rather than isolate them and consider them individually.Multivariate analysis enables researchers to explore the jointperformance of such variables and to determine the effect of eachvariable in the presence of the others. The Second Edition of AlvinRencher's Methods of Multivariate Analysis provides studentsof all statistical backgrounds with both the fundamental and moresophisticated skills necessary to master the discipline. To illustrate multivariate applications, the author providesexamples and exercises based on fifty-nine real data sets from awide variety of scientific fields. Rencher takes a "methods"approach to his subject, with an emphasis on how students andpractitioners can employ multivariate analysis in real-lifesituations. The Second Edition contains revised and updatedchapters from the critically acclaimed First Edition as well asbrand-new chapters on: Cluster analysis Multidimensional scaling Correspondence analysis Biplots Each chapter contains exercises, with corresponding answers andhints in the appendix, providing students the opportunity to testand extend their understanding of the subject. Methods ofMultivariate Analysis provides an authoritative reference forstatistics students as well as for practicing scientists andclinicians.

Discrete Distributions

Applications in the Health Sciences
Author: Daniel Zelterman
Publisher: John Wiley & Sons
ISBN: 0470868899
Category: Mathematics
Page: 306
View: 2694

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There have been many advances in the theory and applications of discrete distributions in recent years. They can be applied to a wide range of problems, particularly in the health sciences, although a good understanding of their properties is very important. Discrete Distributions: Applications in the Health Sciences describes a number of new discrete distributions that arise in the statistical examination of real examples. For each example, an understanding of the issues surrounding the data provides the motivation for the subsequent development of the statistical models. Provides an overview of discrete distributions and their applications in the health sciences. Focuses on real examples, giving readers an insight into the utility of the models. Describes the properties of each distribution, and the methods that led to their development. Presents a range of examples from the health sciences, including cancer, epidemiology, and demography. Features discussion of software implementation – in SAS, Fortran and R – enabling readers to apply the methods to their own problems. Written in an accessible style, suitable for applied statisticians and numerate health scientists. Software and data sets are made available on the Web. Discrete Distributions: Applications in the Health Sciences provides a practical introduction to these powerful statistical tools and their applications, suitable for researchers and graduate students from statistics and biostatistics. The focus on applications, and the accessible style of the book, make it an excellent practical reference source for practitioners from the health sciences.