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RECORD NUMBER: 43 OF 135
OLS Field Name  OLS Field Data  

Main Title  Categorical data analysis /  
Author  Agresti, Alan.  
Publisher  Wiley,  
Year Published  2013  
OCLC Number  786161547  
ISBN  9780470463635; 0470463635  
Subjects  Multivariate analysis. ; MATHEMATICSProbability & StatisticsGeneral  
Holdings 


Edition  3rd ed.  
Collation  xvi, 714 pages : illustrations ; 27 cm.  
Notes  Includes bibliographical references and indexes. 

Contents Notes  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. 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 ; 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 methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control. 