Item Details
Categorical Data Analysis [electronic resource]
Alan Agresti
 Format
 EBook; Book; Online
 Published
 Hoboken, N.J. : WileyInterscience, c2013
 Edition
 3rd ed
 Language
 English
 Related Title
 Virtual Library of Virginia EBL DDA purchased title
 Series
 Wiley Series in Probability and Statistics
 ISBN
 1118710940 (electronic bk.), 9781118710944 (electronic bk.), 9780470463635 (hardback), 0470463635 (hardback), 9781118710852 (electronic bk.), 1118710851 (electronic bk.)
 Summary
 "A classic in its own right, this book continues to provide an introduction to modern generalized linear models for categorical variables. The text emphasizes methods that are most commonly used in practical application, such as classical inferences for two and threeway contingency tables, logistic regression, loglinear models, models for multinomial (nominal and ordinal) responses, and methods for repeated measurement and other forms of clustered, correlated response data. Chapter headings remain essentially with the exception of a new one on Bayesian inference for parametric models. Other major changes include an expansion of clustered data, new research on analysis of data sets with robust variables, extensive discussions of ordinal data, more on interpretation, and additional exercises throughout the book. R and SAS are now showcased as the software of choice. An author web site with solutions, commentaries, software programs, and data sets is available"
 Contents
 Introduction: Distributions and inference for categorical data
 Describing contingency tables
 Inference for twoway contingency tables
 Introduction to generalized linear models
 Logistic regression
 Building, checking, and applying logistic regression models
 Alternative modeling of binary response data
 Models for multinomial responses
 Loglinear models for contingency tables
 Building and extending loglinear models
 Models for matched pairs
 Clustered categorical data: marginal and transitional models
 Clustered categorical data: random effects models
 Other mixture models for discrete data
 Nonmodelbased classification and clustering
 Large and smallsample theory for multinomial models
 Historical tour of categorical data analysis
 Appendix A: Statistical software for categorical data analysis
 Appendix B: Chisquared distribution values.
 Description
 1 online resource (xvi, 714 p.) ill.
 Notes
 Machine generated contents note: Preface 1. Introduction: Distributions and Inference for Categorical Data 1 1.1 Categorical Response Data, 1 1.2 Distributions for Categorical Data 1.3 Statistical Inference for Categorical Data 1.4 Statistical Inference for Binomial Parameters 1.5 Statistical Inference for Multinomial Parameters 1.6 Bayesian Inference for Binomial and Multinomial Parameters Notes Exercises 2. Describing Contingency Tables 2.1 Probability Structure for Contingency Tables 2.2 Comparing Two Proportions 2.3 Conditional Association in Stratified 2x2 Tables 2.4 Measuring Association in I x J Tables Notes Exercises 3. Inference for TwoWay Contingency Tables 3.1 Confidence Intervals for Association Parameters 3.2 Testing Independence in TwoWay Contingency Tables 3.3 FollowingUp ChiSquared Tests 3.4 TwoWay Tables with Ordered Classifications 3.5 SmallSample Inference for Contingency Tables 3.6 Bayesian Inference for TwoWay Contingency Tables 3.7 Extensions for Multiway Tables and Nontabulated Responses Notes Exercises 4. Introduction to Generalized Linear Models 4.1 The Generalized Linear Model 4.2 Generalized Linear Models for Binary Data 4.3 Generalized Linear Models for Counts and Rates 4.4 Moments and Likelihood for Generalized Linear Models 4.5 Inference and Model Checking for Generalized Linear Models 4.6 Fitting Generalized Linear Models 4.7 QuasiLikelihood and Generalized Linear Models Notes Exercises 5. Logistic Regression 5.1 Interpreting Parameters in Logistic Regression 5.2 Inference for Logistic Regression 5.3 Logistic Models with Categorical Predictors 5.4 Multiple Logistic Regression 5.5 Fitting Logistic Regression Models Notes Exercises 6. Building, Checking, and Applying Logistic Regression Models 6.1 Strategies in Model Selection 6.2 Logistic Regression Diagnostics 6.3 Summarizing the Predictive Power of a Model 6.3 MantelHaenszel and Related Methods for Multiple 2x2 Tables 6.4 Detecting and Dealing with Infinite Estimates 6.5 Sample Size and Power Considerations Notes Exercises 7. Alternative Modeling of Binary Response Data 7.1 Probit and Complementary LogLog Models 7.2 Bayesian Inference for Binary Regression 7.3 Conditional Logistic Regression 7.4 Smoothing: Kernels, Penalized Likelihood, Generalized Additive Models 7.5 Issues in Analyzing HighDimensional Categorical Data Notes Exercises 8. Models for Multinomial Responses 8.1 Nominal Responses: BaselineCategory Logit Models 8.2 Ordinal Responses: Cumulative Logit Models 8.3 Ordinal Responses: Alternative Models 8.4 Testing Conditional Independence in I ? J ? K Tables 8.5 DiscreteChoice Models 8.6 Bayesian Modeling of Multinomial Responses Notes Exercises 9. Loglinear Models for Contingency Tables 9.1 Loglinear Models for TwoWay Tables 9.2 Loglinear Models for Independence and Interaction in ThreeWay Tables 9.3 Inference for Loglinear Models 9.4 Loglinear Models for Higher Dimensions 9.5 The Loglinear?Logistic Model Connection 9.6 Loglinear Model Fitting: Likelihood Equations and Asymptotic Distributions 9.7 Loglinear Model Fitting: Iterative Methods and their Application Notes Exercises 10. Building and Extending Loglinear Models 10.1 Conditional Independence Graphs and Collapsibility 10.2 Model Selection and Comparison 10.3 Residuals for Detecting CellSpecific Lack of Fit 10.4 Modeling Ordinal Associations 10.5 Generalized Loglinear and Association Models, Correlation Models, and Correspondence Analysis 10.6 Empty Cells and Sparseness in Modeling Contingency Tables 10.7 Bayesian Loglinear Modeling Notes Exercises 11. Models for Matched Pairs 11.1 Comparing Dependent Proportions 11.2 Conditional Logistic Regression for Binary Matched Pairs 11.3 Marginal Models for Square Contingency Tables 11.4 Symmetry, Quasisymmetry, and Quasiindependence 11.5 Measuring Agreement Between Observers 11.6 BradleyTerry Model for Paired Preferences 11.7 Marginal Models and Quasisymmetry Models for Matched Sets Notes Exercises 12. Clustered Categorical Data: Marginal and Transitional Models 12.1 Marginal Modeling: Maximum Likelihood Approach 12.2 Marginal Modeling: Generalized Estimating Equations Approach 12.3 Quasilikelihood and Its GEE Multivariate Extension: Details 12.4 Transitional Models: Markov Chain and Time Series Models Notes Exercises 13. Clustered Categorical Data: Random Effects Models 13.1 Random Effects Modeling of Clustered Categorical Data 13.2 Binary Responses: The LogisticNormal Model 13.3 Examples of Random Effects Models for Binary Data 13.4 Random Effects Models for Multinomial Data 13.5 Multilevel Models 13.6 GLMM Fitting, Inference, and Prediction 13.7 Bayesian Multivariate Categorical Modeling Notes Exercises 14. Other Mixture Models for Discrete Data 14.1 Latent Class Models 14.2 Nonparametric Random Effects Models 14.3 BetaBinomial Models 14.4 Negative Binomial Regression 14.5 Poisson Regression with Random Effects Notes Exercises 15. NonModelBased Classification and Clustering 15.2 Classification: Linear Discriminant Analysis 15.3 Classification: TreeStructured Prediction 15.4 Cluster Analysis for Categorical Data Notes Exercises 16. Large and SmallSample Theory for Parametric Models 16.1 Delta Method 16.2 Asymptotic Distributions of Estimators of Model Parameters and Cell Probabilities 16.3 Asymptotic Distributions of Residuals and GoodnessofFit Statistics 16.4 Asymptotic Distributions for Logit/Loglinear Models 16.5 SmallSample Significance Tests for Contingency Tables 16.6 SmallSample Confidence Intervals for Categorical Data 16.7 Alternative Estimation Theory for Parametric Models Notes Exercises 17. Historical Tour of Categorical Data Analysis 17.1 PearsonYule Association Controversy 17.2 R. A. Fisher's Contributions 17.3 Logistic Regression 17.4 Multiway Contingency Tables and Loglinear Models 17.5 Bayesian Methods for Categorical Data 17.6 A Look Forward, and Backward Appendix A. Statistical Software for Categorical Data Analysis Appendix B. ChiSquared Distribution Values References Author Index Example Index Subject Index.
 Includes bibliographical references and indexes.
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LEADER 10029cam a2200601La 4500001 ocn840466744003 OCoLC005 20140508125930.7006 m d007 cr n008 130421s2013 njua ob 001 0 eng da YDXCP c YDXCP d OCLCO d RECBK d N$Ta 879202657a 1118710940 (electronic bk.)a 9781118710944 (electronic bk.)z 9780470463635 (hardback)z 0470463635 (hardback)a 9781118710852 (electronic bk.)a 1118710851 (electronic bk.)a AU@ b 000051748306a (OCoLC)840466744 z (OCoLC)879202657a QA278 b .A353 2013a MAT x 003000 2 bisacsha MAT x 029000 2 bisacsha 519.5/35 2 23a MAT029000 2 bisacsha MAINa Agresti, Alan.a Categorical data analysis h [electronic resource] / c Alan Agresti.a 3rd ed.a Hoboken, N.J. : b WileyInterscience, c c2013a 1 online resource (xvi, 714 p.) b ill.a text b txt 2 rdacontenta computer b c 2 rdamediaa online resource b cr 2 rdacarriera Wiley series in probability and statisticsa "A classic in its own right, this book continues to provide an introduction to modern generalized linear models for categorical variables. The text emphasizes methods that are most commonly used in practical application, such as classical inferences for two and threeway contingency tables, logistic regression, loglinear models, models for multinomial (nominal and ordinal) responses, and methods for repeated measurement and other forms of clustered, correlated response data. Chapter headings remain essentially with the exception of a new one on Bayesian inference for parametric models. Other major changes include an expansion of clustered data, new research on analysis of data sets with robust variables, extensive discussions of ordinal data, more on interpretation, and additional exercises throughout the book. R and SAS are now showcased as the software of choice. An author web site with solutions, commentaries, software programs, and data sets is available" c Provided by publisher.a Includes bibliographical references and indexes.a Machine generated contents note: Preface 1. Introduction: Distributions and Inference for Categorical Data 1 1.1 Categorical Response Data, 1 1.2 Distributions for Categorical Data 1.3 Statistical Inference for Categorical Data 1.4 Statistical Inference for Binomial Parameters 1.5 Statistical Inference for Multinomial Parameters 1.6 Bayesian Inference for Binomial and Multinomial Parameters Notes Exercises 2. Describing Contingency Tables 2.1 Probability Structure for Contingency Tables 2.2 Comparing Two Proportions 2.3 Conditional Association in Stratified 2x2 Tables 2.4 Measuring Association in I x J Tables Notes Exercises 3. Inference for TwoWay Contingency Tables 3.1 Confidence Intervals for Association Parameters 3.2 Testing Independence in TwoWay Contingency Tables 3.3 FollowingUp ChiSquared Tests 3.4 TwoWay Tables with Ordered Classifications 3.5 SmallSample Inference for Contingency Tables 3.6 Bayesian Inference for TwoWay Contingency Tables 3.7 Extensions for Multiway Tables and Nontabulated Responses Notes Exercises 4. Introduction to Generalized Linear Models 4.1 The Generalized Linear Model 4.2 Generalized Linear Models for Binary Data 4.3 Generalized Linear Models for Counts and Rates 4.4 Moments and Likelihood for Generalized Linear Models 4.5 Inference and Model Checking for Generalized Linear Models 4.6 Fitting Generalized Linear Models 4.7 QuasiLikelihood and Generalized Linear Models Notes Exercises 5. Logistic Regression 5.1 Interpreting Parameters in Logistic Regression 5.2 Inference for Logistic Regression 5.3 Logistic Models with Categorical Predictors 5.4 Multiple Logistic Regression 5.5 Fitting Logistic Regression Models Notes Exercises 6. Building, Checking, and Applying Logistic Regression Models 6.1 Strategies in Model Selection 6.2 Logistic Regression Diagnostics 6.3 Summarizing the Predictive Power of a Model 6.3 MantelHaenszel and Related Methods for Multiple 2x2 Tables 6.4 Detecting and Dealing with Infinite Estimates 6.5 Sample Size and Power Considerations Notes Exercises 7. Alternative Modeling of Binary Response Data 7.1 Probit and Complementary LogLog Models 7.2 Bayesian Inference for Binary Regression 7.3 Conditional Logistic Regression 7.4 Smoothing: Kernels, Penalized Likelihood, Generalized Additive Models 7.5 Issues in Analyzing HighDimensional Categorical Data Notes Exercises 8. Models for Multinomial Responses 8.1 Nominal Responses: BaselineCategory Logit Models 8.2 Ordinal Responses: Cumulative Logit Models 8.3 Ordinal Responses: Alternative Models 8.4 Testing Conditional Independence in I ? J ? K Tables 8.5 DiscreteChoice Models 8.6 Bayesian Modeling of Multinomial Responses Notes Exercises 9. Loglinear Models for Contingency Tables 9.1 Loglinear Models for TwoWay Tables 9.2 Loglinear Models for Independence and Interaction in ThreeWay Tables 9.3 Inference for Loglinear Models 9.4 Loglinear Models for Higher Dimensions 9.5 The Loglinear?Logistic Model Connection 9.6 Loglinear Model Fitting: Likelihood Equations and Asymptotic Distributions 9.7 Loglinear Model Fitting: Iterative Methods and their Application Notes Exercises 10. Building and Extending Loglinear Models 10.1 Conditional Independence Graphs and Collapsibility 10.2 Model Selection and Comparison 10.3 Residuals for Detecting CellSpecific Lack of Fit 10.4 Modeling Ordinal Associations 10.5 Generalized Loglinear and Association Models, Correlation Models, and Correspondence Analysis 10.6 Empty Cells and Sparseness in Modeling Contingency Tables 10.7 Bayesian Loglinear Modeling Notes Exercises 11. Models for Matched Pairs 11.1 Comparing Dependent Proportions 11.2 Conditional Logistic Regression for Binary Matched Pairs 11.3 Marginal Models for Square Contingency Tables 11.4 Symmetry, Quasisymmetry, and Quasiindependence 11.5 Measuring Agreement Between Observers 11.6 BradleyTerry Model for Paired Preferences 11.7 Marginal Models and Quasisymmetry Models for Matched Sets Notes Exercises 12. Clustered Categorical Data: Marginal and Transitional Models 12.1 Marginal Modeling: Maximum Likelihood Approach 12.2 Marginal Modeling: Generalized Estimating Equations Approach 12.3 Quasilikelihood and Its GEE Multivariate Extension: Details 12.4 Transitional Models: Markov Chain and Time Series Models Notes Exercises 13. Clustered Categorical Data: Random Effects Models 13.1 Random Effects Modeling of Clustered Categorical Data 13.2 Binary Responses: The LogisticNormal Model 13.3 Examples of Random Effects Models for Binary Data 13.4 Random Effects Models for Multinomial Data 13.5 Multilevel Models 13.6 GLMM Fitting, Inference, and Prediction 13.7 Bayesian Multivariate Categorical Modeling Notes Exercises 14. Other Mixture Models for Discrete Data 14.1 Latent Class Models 14.2 Nonparametric Random Effects Models 14.3 BetaBinomial Models 14.4 Negative Binomial Regression 14.5 Poisson Regression with Random Effects Notes Exercises 15. NonModelBased Classification and Clustering 15.2 Classification: Linear Discriminant Analysis 15.3 Classification: TreeStructured Prediction 15.4 Cluster Analysis for Categorical Data Notes Exercises 16. Large and SmallSample Theory for Parametric Models 16.1 Delta Method 16.2 Asymptotic Distributions of Estimators of Model Parameters and Cell Probabilities 16.3 Asymptotic Distributions of Residuals and GoodnessofFit Statistics 16.4 Asymptotic Distributions for Logit/Loglinear Models 16.5 SmallSample Significance Tests for Contingency Tables 16.6 SmallSample Confidence Intervals for Categorical Data 16.7 Alternative Estimation Theory for Parametric Models Notes Exercises 17. Historical Tour of Categorical Data Analysis 17.1 PearsonYule Association Controversy 17.2 R. A. Fisher's Contributions 17.3 Logistic Regression 17.4 Multiway Contingency Tables and Loglinear Models 17.5 Bayesian Methods for Categorical Data 17.6 A Look Forward, and Backward Appendix A. Statistical Software for Categorical Data Analysis Appendix B. ChiSquared Distribution Values References Author Index Example Index Subject Index.a Introduction: Distributions and inference for categorical data  Describing contingency tables  Inference for twoway contingency tables  Introduction to generalized linear models  Logistic regression  Building, checking, and applying logistic regression models  Alternative modeling of binary response data  Models for multinomial responses  Loglinear models for contingency tables  Building and extending loglinear models  Models for matched pairs  Clustered categorical data: marginal and transitional models  Clustered categorical data: random effects models  Other mixture models for discrete data  Nonmodelbased classification and clustering  Large and smallsample theory for multinomial models  Historical tour of categorical data analysis  Appendix A: Statistical software for categorical data analysis  Appendix B: Chisquared distribution values.a Multivariate Analysis.a Multivariate analysis.a MATHEMATICS / Probability & Statistics / General. 2 bisacsha Multivariate analysis. 2 fast 0 (OCoLC)fst01029105a Electronic books.a Virtual Library of Virginia EBL DDA purchased titlec Original z 9780470463635 z 0470463635 w (DLC) 2012009792a Wiley series in probability and statistics.u http://proxy.its.virginia.edu/login?url=http://viva.eblib.com/patron/FullRecord.aspx?p=1168529&userid=^u&conl=UVA&echo=1a YBP Library Services b YANK n 10451057a Recorded Books, LLC b RECE n rbeEB00064151a EBSCOhost b EBSC n 769330a 92 b VA@w WEB l INTERNET m UVALIB t INTERNET
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