CategoricalDataAnalysis.pdf

The explosion in the development of methods for analyzing categorical data that began in the 1960s has continued apace in recent years. This book provides an overview of these methods, as well as older, now standard, methods. It gives special emphasis to generalized linear modeling techniques, which extend linear model methods for continuous variables, and their extensions for multivariate responses.
Categorical Data analysis WILEY SERIES IN PROBABILITY AND STATISTICS Established by WALTER A sheWHART and samuel s WIlks Editors: David /. Balding Noel A C Cressie, Garrett M. Fitzmauric Harvey Goldstein, lain M. Johnstone, Geert Molenberghs, David w. Scott, Adrian F M. Smith, Ruey S Tsay, Sanford Weisberg Editors Emeriti: Vic Barnett, Stuart Hunter, Joseph b. Kadane, Jozef L. Teugels A complete list of the titles in this series appears at the end of this volume Categorical Data Analysis Third edition ALAN AGRESTI Department of Statistics University of Florida Gainesville. Florida EiWILEYINTERSCIENCE A JoHn WILEY SONS INC. PUBLICATION Cover Image: (back ground) Peter Firus/iStockphoto, (line art) courtesy of the author Copyright@ 2013 by John Wiley Sons All rights reserved Published by John Wiley Sons, Inc, Hoboken, New Jersey Published simultaneously in Canada No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per copy fee to the Copyright Clearance Center, Inc. 222 Rosewood Drive Danvers MA 01923. 9787508400. fax 9787504470. or on the web at www.copyright.comRequeststothePublisherforpermissionshouldbeaddressedtothePermissions Department, John Wiley Sons, Inc 1 1 1 River Street, Hoboken, NJ 07030, 2017486011, fax 2017486008 oronlineathttp://www.wiley.com/go/permission Limit of Liability/Disclaimer of warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages For general information on our other products and services or for technical support, please contact our Cu Care Department within the United States at 8007622974, outside the United States at 3175723993 ortomer fax3l75724002 Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be availableinelectronicformatsFormoreinformationaboutWileyproductsvisitourwebsiteatwww.wiley.com Library of Congress CataloginginPublication data Agresti, Alan Categorical data analysis /alan a grest. 3rd ed p. cm. (Wiley series in probability and statistics; 792) Includes bibliographical references and index isbn 9780470463635(hardback) 1. Multivariate analysis. I. Title QA278.A3532013 519.5′35dc23 2012009792 Printed in the United States of America 098765432 To Jacki Contents Preface XIII I Introduction: Distributions and Inference for Categorical Data 1. 1 Categorical Response data, I 1. 2 Distributions for Categorical Data, 5 1. 3 Statistical Inference for Categorical data 8 1.4 Statistical inference for binomial parameters 13 1.5 Statistical Inference for multinomial parameters. 17 1.6 Bayesian Inference for Binomial and Multinomial Parameters, 22 Notes, 27 Exercises. 28 2 Describing Contingency Tables 37 2. I Probability Structure for Contingency Tables, 37 2.2 Comparing Two Proportions, 43 2,3 Conditional Association in Stratified 2 x 2 Tables. 47 2. 4 Measuring Association in /x J Tables, 54 Notes. 60 Exercises. 60 3 Inference for TwoWay Contingency Tables 3.1 Confidence Intervals for Association parameters, 69 3.2 Testing Independence in Twoway contingency tables, 75 3.3 Followingup ChiSquared Tests, 80 3. 4 TwoWay Tables with Ordered Classifications, 86 3.5 SmallSample Inference for Contingency Tables, 90 3.6 Bayesian Inference for Twoway Contingency Tables, 96 3.7 Extensions for Multiway Tables and Nontabulated Responses, 100 Notes. 101 Exercises. 103 VIll CONTENTS Introduction to Generalized Linear Models 113 4.1 The generalized linear model. 113 4.2 Generalized Linear Models for binary data, 117 4.3 Generalized Linear Models for Counts and rates 122 4.4 Moments and Likelihood for Generalized Linear models. 130 4.5 Inference and Model Checking for Generalized Linear Models, 136 4.6 Fitting Generalized Linear Models, 143 4.7 Quasi Likelihood and Generalized Linear Models, 149 Notes. 152 Exercises. 153 5 Logistic Regression 163 5.1 Interpreting Parameters in Logistic Regression, 163 5.2 Inference for Logistic Regression, 169 5.3 Logistic Models with Categorical Predictors, 175 5.4 Multiple Logistic Regression, 182 5.5 Fitting Logistic Regression Models, 192 Notes. 195 Exercises. 196 6 Building, Checking, and applying logistic Regression Models 207 6. 1 Strategies in Model Selection, 207 6.2 Logistic Regression Diagnostics, 215 6.3 Summarizing the predictive Power of a Model, 221 6.4 MantelHaenszel and Related Methods for Multiple 2x 2 Tables, 225 6.5 Detecting and Dealing with Infinite Estimates, 233 6.6 Sample Size and Power Considerations, 237 Notes. 241 Exercises. 243 7 Alternative Modeling of Binary Response Data 251 7.1 Probit and Complementary Loglog Models, 251 7.2 Bayesian Inference for Binary Regression, 257 7.3 Conditional Logistic Regression, 265 7.4 Smoothing: Kernels, Penalized Likelihood, Generalized Additive models. 270 7.5 Issues in Analyzing HighDimensional Categorical Data, 278 Notes. 285 Exercises. 287
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Categorical Data Analysis
20170118Praise for the Second Edition: 'A musthave book for anyone expecting to do research and/or applications in categorical data analysis'. ('Statistics in Medicine'). 'It is a total delight reading this book'. ('Pharmaceutical Research'). 'If you do any analysis of categorical data, this is an essential desktop reference'. ('Technometrics'). The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis. 'Categorical Data Analysis, Third Edition' summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features: an emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models; two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis; new sections introducing the Bayesian approach for methods in that chapter; more than 100 analyses of data sets and over 600 exercises; notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources; and, a supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions. 'Categorical Data Analysis, Third Edition' is an invaluable tool for statisticians and methodologist
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定性数据分析英文第三版高清版 Categorical Data Analysis 3rd Edition by ALAN AGRESTI
20180405Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. ALAN AGRESTI is Distinguished Professor Emeritus in the Department of Statistics at the University of Florida. He has presented short courses on categorical data methods in thirty countries.
 2.94MB
Categorical Data Analysis 2nd Edition  Agresti
20111022A valuable new edition of a standard reference "A 'musthave' book for anyone expecting to do research and/or applications in categorical data analysis." –Statistics in Medicine on Categorical Data Analysis, First Edition The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. Responding to new developments in the field as well as to the needs of a new generation of professionals and students, this new edition of the classic Categorical Data Analysis offers a comprehensive introduction to the most important methods for categorical data analysis. Designed for statisticians and biostatisticians as well as scientists and graduate students practicing statistics, Categorical Data Analysis, Second Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial regression for discrete data with normal regression for continuous data. Adding to the value in the new edition is coverage of: Three new chapters on methods for repeated measurement and other forms of clustered categorical data, including marginal models and associated generalized estimating equations (GEE) methods, and mixed models with random effects Stronger emphasis on logistic regression modeling of binary and multicategory data An appendix showing the use of SAS for conducting nearly all analyses in the book Prescriptions for how ordinal variables should be treated differently than nominal variables Discussion of exact smallsample procedures More than 100 analyses of real data sets to illustrate application of the methods, and more than 600 exercises An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
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Categorical Data Analysis Using The SAS System
20091106Chapter 1. Introduction 1 1.1 Overview . . . . . . . ....1.2 Scale of Measurement ....1.3 Sampling Frameworks ....1.4 OverviewofAnalysis Strategies ....1.5 WorkingwithTables in ...11.4 Analysis ofPainStudy . . . . ...
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Categorical Data Analysis Using SAS(3rd) 无水印原版pdf
20180508Categorical Data Analysis Using SAS(3rd) 英文无水印原版pdf 第3版 pdf所有页面使用FoxitReader、PDFXChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除...
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Categorical Data Analysis by Example
20170920Introduces the key concepts in the analysis of categoricaldata with illustrative examples and accompanying R code This book is aimed at all those who wish to discover how to analyze categorical data without getting immersed in complicated mathematics and without needing to wade through a large amount of prose. It is aimed at researchers with their own data ready to be analyzed and at students who would like an approachable alternative view of the subject. Each new topic in categorical data analysis is illustrated with an example that readers can apply to their own sets of data. In many cases, R code is given and excerpts from the resulting output are presented. In the context of loglinear models for crosstabulations, two specialties of the house have been included: the use of cobweb diagrams to get visual information concerning significant interactions, and a procedure for detecting outlier category combinations. The R code used for these is available and may be freely adapted. In addition, this book: • Uses an example to illustrate each new topic in categorical data • Provides a clear explanation of an important subject • Is understandable to most readers with minimal statistical and mathematical backgrounds • Contains examples that are accompanied by R code and resulting output • Includes starred sections that provide more background details for interested readers Categorical Data Analysis by Example is a reference for students in statistics and researchers in other disciplines, especially the social sciences, who use categorical data. This book is also a reference for practitioners in market research, medicine, and other fields. GRAHAM J. G. UPTON is formerly Professor of Applied Statistics, Department of Mathematical Sciences, University of Essex. Dr. Upton is author of The Analysis of Crosstabulated Data (1978) and joint author of Spatial Data Analysis by Example (2 volumes, 1995), both published by Wiley. He is the lead author of The Oxford Dictionary of Statistics (OUP, 2014). His books have been translated into Japanese, Russian, and Welsh.
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定性数据分析Categorical Data Analysis 2nd Edition  Agresti
20161005分享产生价值！ A valuable new edition of a standard reference "A 'musthave' book for anyone expecting to do research and/or applications in categorical data analysis." –Statistics in Medicine on Categorical Data Analysis, First Edition The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. Responding to new developments in the field as well as to the needs of a new generation of professionals and students, this new edition of the classic Categorical Data Analysis offers a comprehensive introduction to the most important methods for categorical data analysis. Designed for statisticians and biostatisticians as well as scientists and graduate students practicing statistics, Categorical Data Analysis, Second Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial regression for discrete data with normal regression for continuous data. Adding to the value in the new edition is coverage of: Three new chapters on methods for repeated measurement and other forms of clustered categorical data, including marginal models and associated generalized estimating equations (GEE) methods, and mixed models with random effects Stronger emphasis on logistic regression modeling of binary and multicategory data An appendix showing the use of SAS for conducting nearly all analyses in the book Prescriptions for how ordinal variables should be treated differently than nominal variables Discussion of exact smallsample procedures More than 100 analyses of real data sets to illustrate application of the methods, and more than 600 exercises An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
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