Categorical Data Analysis
by Agresti, AlanEdition: 3rd
Format: Hardcover
Pub. Date: 2012-12-03
Publisher(s): Wiley
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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 | |
| Table of Contents provided by Publisher. All Rights Reserved. |
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