Categorical Data Analysis

by Agresti, Alan

• ISBN13: 9780470463635
• ISBN10: 0470463635
• eBook ISBN(s): 9781118467756
• Edition: 3rd
• Format: Hardcover
• Copyright: 2012-12-03
• Publisher: Wiley
• More Book Details
• Purchase Benefits

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 three-way 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.

Author Biography

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. He is the author of five other books, including An Introduction to Categorical Data Analysis, Second Edition and Analysis of Ordinal Categorical Data, Second Edition, both published by Wiley.

Table of Contents

Preface
Introduction: Distributions and Inference for Categorical Data	p. 1
Categorical Response Data	p. 1
Distributions for Categorical Data
Statistical Inference for Categorical Data
Statistical Inference for Binomial Parameters
Statistical Inference for Multinomial Parameters
Bayesian Inference for Binomial and Multinomial Parameters Notes Exercises
Describing Contingency Tables
Probability Structure for Contingency Tables
Comparing Two Proportions
Conditional Association in Stratified 2x2 Tables
Measuring Association in I x J Tables Notes Exercises
Inference for Two-Way Contingency Tables
Confidence Intervals for Association Parameters
Testing Independence in Two-Way Contingency Tables
Following-Up Chi-Squared Tests
Two-Way Tables with Ordered Classifications
Small-Sample Inference for Contingency Tables
Bayesian Inference for Two-Way Contingency Tables
Extensions for Multiway Tables and Nontabulated Responses Notes Exercises
Introduction to Generalized Linear Models
The Generalized Linear Model
Generalized Linear Models for Binary Data
Generalized Linear Models for Counts and Rates
Moments and Likelihood for Generalized Linear Models
Inference and Model Checking for Generalized Linear Models
Fitting Generalized Linear Models
Quasi-Likelihood and Generalized Linear Models Notes Exercises
Logistic Regression
Interpreting Parameters in Logistic Regression
Inference for Logistic Regression
Logistic Models with Categorical Predictors
Multiple Logistic Regression
Fitting Logistic Regression Models Notes Exercises
Building, Checking, and Applying Logistic Regression Models
Strategies in Model Selection
Logistic Regression Diagnostics
Summarizing the Predictive Power of a Model
Mantel-Haenszel and Related Methods for Multiple 2x2 Tables
Detecting and Dealing with Infinite Estimates
Sample Size and Power Considerations Notes Exercises
Alternative Modeling of Binary Response Data
Probit and Complementary Log-Log Models
Bayesian Inference for Binary Regression
Conditional Logistic Regression
Smoothing: Kernels, Penalized Likelihood, Generalized Additive Models
Issues in Analyzing High-Dimensional Categorical Data Notes Exercises
Models for Multinomial Responses
Nominal Responses: Baseline-Category Logit Models
Ordinal Responses: Cumulative Logit Models
Ordinal Responses: Alternative Models
Testing Conditional Independence in I ? J ? K Tables
Discrete-Choice Models
Bayesian Modeling of Multinomial Responses Notes Exercises
Loglinear Models for Contingency Tables
Loglinear Models for Two-Way Tables
Loglinear Models for Independence and Interaction in Three-Way Tables
Inference for Loglinear Models
Loglinear Models for Higher Dimensions
The Loglinear?Logistic Model Connection
Loglinear Model Fitting: Likelihood Equations and Asymptotic Distributions
Loglinear Model Fitting: Iterative Methods and their Application Notes Exercises
Building and Extending Loglinear Models
Conditional Independence Graphs and Collapsibility
Model Selection and Comparison
Residuals for Detecting Cell-Specific Lack of Fit
Modeling Ordinal Associations
Generalized Loglinear and Association Models, Correlation Models, and Correspondence Analysis
Empty Cells and Sparseness in Modeling Contingency Tables
Bayesian Loglinear Modeling Notes Exercises
Models for Matched Pairs
Comparing Dependent Proportions
Conditional Logistic Regression for Binary Matched Pairs
Marginal Models for Square Contingency Tables
Symmetry, Quasi-symmetry, and Quasi-independence
Measuring Agreement Between Observers
Bradley-Terry Model for Paired Preferences
Marginal Models and Quasi-symmetry Models for Matched Sets Notes Exercises
Clustered Categorical Data: Marginal and Transitional Models
Marginal Modeling: Maximum Likelihood Approach
Marginal Modeling: Generalized Estimating Equations Approach
Quasi-likelihood and Its GEE Multivariate Extension: Details
Transitional Models: Markov Chain and Time Series Models Notes Exercises
Clustered Categorical Data: Random Effects Models
Random Effects Modeling of Clustered Categorical Data
Binary Responses: The Logistic-Normal Model
Examples of Random Effects Models for Binary Data
Random Effects Models for Multinomial Data
Multilevel Models
GLMM Fitting, Inference, and Prediction
Bayesian Multivariate Categorical Modeling Notes Exercises
Other Mixture Models for Discrete Data
Latent Class Models
Nonparametric Random Effects Models
Beta-Binomial Models
Negative Binomial Regression
Poisson Regression with Random Effects Notes Exercises
Non-Model-Based Classification and Clustering
Classification: Linear Discriminant Analysis
Classification: Tree-Structured Prediction
Cluster Analysis for Categorical Data Notes Exercises
Large- and Small-Sample Theory for Parametric Models
Delta Method
Asymptotic Distributions of Estimators of Model Parameters and Cell Probabilities
Asymptotic Distributions of Residuals and Goodness-of-Fit Statistics
Asymptotic Distributions for Logit/Loglinear Models
Small-Sample Significance Tests for Contingency Tables
Small-Sample Confidence Intervals for Categorical Data
Alternative Estimation Theory for Parametric Models Notes Exercises
Historical Tour of Categorical Data Analysis
Pearson-Yule Association Controversy
R. A. Fisher's Contributions
Logistic Regression
Multiway Contingency Tables and Loglinear Models
Bayesian Methods for Categorical Data
A Look Forward, and Backward
Statistical Software for Categorical Data Analysis
Chi-Squared Distribution Values
References
Author Index
Example Index
Subject Index
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