| General note |
TABLE OF CONTENTS<br/>Preface xiii<br/><br/>1 Introduction: Distributions and Inference for Categorical Data 1<br/><br/>1.1 Categorical Response Data, 1<br/><br/>1.2 Distributions for Categorical Data, 5<br/><br/>1.3 Statistical Inference for Categorical Data, 8<br/><br/>1.4 Statistical Inference for Binomial Parameters, 13<br/><br/>1.5 Statistical Inference for Multinomial Parameters, 17<br/><br/>1.6 Bayesian Inference for Binomial and Multinomial Parameters, 22<br/><br/>Notes, 27<br/><br/>Exercises, 28<br/><br/>2 Describing Contingency Tables 37<br/><br/>2.1 Probability Structure for Contingency Tables, 37<br/><br/>2.2 Comparing Two Proportions, 43<br/><br/>2.3 Conditional Association in Stratified 2 × 2 Tables, 47<br/><br/>2.4 Measuring Association in I × J Tables, 54<br/><br/>Notes, 60<br/><br/>Exercises, 60<br/><br/>3 Inference for Two-Way Contingency Tables 69<br/><br/>3.1 Confidence Intervals for Association Parameters, 69<br/><br/>3.2 Testing Independence in Two-way Contingency Tables, 75<br/><br/>3.3 Following-up Chi-Squared Tests, 80<br/><br/>3.4 Two-Way Tables with Ordered Classifications, 86<br/><br/>3.5 Small-Sample Inference for Contingency Tables, 90<br/><br/>3.6 Bayesian Inference for Two-way Contingency Tables, 96<br/><br/>3.7 Extensions for Multiway Tables and Nontabulated Responses, 100<br/><br/>Notes, 101<br/><br/>Exercises, 103<br/><br/>4 Introduction to Generalized Linear Models 113<br/><br/>4.1 The Generalized Linear Model, 113<br/><br/>4.2 Generalized Linear Models for Binary Data, 117<br/><br/>4.3 Generalized Linear Models for Counts and Rates, 122<br/><br/>4.4 Moments and Likelihood for Generalized Linear Models, 130<br/><br/>4.5 Inference and Model Checking for Generalized Linear Models, 136<br/><br/>4.6 Fitting Generalized Linear Models, 143<br/><br/>4.7 Quasi-Likelihood and Generalized Linear Models, 149<br/><br/>Notes, 152<br/><br/>Exercises, 153<br/><br/>5 Logistic Regression 163<br/><br/>5.1 Interpreting Parameters in Logistic Regression, 163<br/><br/>5.2 Inference for Logistic Regression, 169<br/><br/>5.3 Logistic Models with Categorical Predictors, 175<br/><br/>5.4 Multiple Logistic Regression, 182<br/><br/>5.5 Fitting Logistic Regression Models, 192<br/><br/>Notes, 195<br/><br/>Exercises, 196<br/><br/>6 Building, Checking, and Applying Logistic Regression Models 207<br/><br/>6.1 Strategies in Model Selection, 207<br/><br/>6.2 Logistic Regression Diagnostics, 215<br/><br/>6.3 Summarizing the Predictive Power of a Model, 221<br/><br/>6.4 Mantel–Haenszel and Related Methods for Multiple 2 × 2 Tables, 225<br/><br/>6.5 Detecting and Dealing with Infinite Estimates, 233<br/><br/>6.6 Sample Size and Power Considerations, 237<br/><br/>Notes, 241<br/><br/>Exercises, 243<br/><br/>7 Alternative Modeling of Binary Response Data 251<br/><br/>7.1 Probit and Complementary Log–log Models, 251<br/><br/>7.2 Bayesian Inference for Binary Regression, 257<br/><br/>7.3 Conditional Logistic Regression, 265<br/><br/>7.4 Smoothing: Kernels, Penalized Likelihood, Generalized Additive Models, 270<br/><br/>7.5 Issues in Analyzing High-Dimensional Categorical Data, 278<br/><br/>Notes, 285<br/><br/>Exercises, 287<br/><br/>8 Models for Multinomial Responses 293<br/><br/>8.1 Nominal Responses: Baseline-Category Logit Models, 293<br/><br/>8.2 Ordinal Responses: Cumulative Logit Models, 301<br/><br/>8.3 Ordinal Responses: Alternative Models, 308<br/><br/>8.4 Testing Conditional Independence in I × J × K Tables, 314<br/><br/>8.5 Discrete-Choice Models, 320<br/><br/>8.6 Bayesian Modeling of Multinomial Responses, 323<br/><br/>Notes, 326<br/><br/>Exercises, 329<br/><br/>9 Loglinear Models for Contingency Tables 339<br/><br/>9.1 Loglinear Models for Two-way Tables, 339<br/><br/>9.2 Loglinear Models for Independence and Interaction in Three-way Tables, 342<br/><br/>9.3 Inference for Loglinear Models, 348<br/><br/>9.4 Loglinear Models for Higher Dimensions, 350<br/><br/>9.5 Loglinear—Logistic Model Connection, 353<br/><br/>9.6 Loglinear Model Fitting: Likelihood Equations and Asymptotic Distributions, 356<br/><br/>9.7 Loglinear Model Fitting: Iterative Methods and Their Application, 364<br/><br/>Notes, 368<br/><br/>Exercises, 369<br/><br/>10 Building and Extending Loglinear Models 377<br/><br/>10.1 Conditional Independence Graphs and Collapsibility, 377<br/><br/>10.2 Model Selection and Comparison, 380<br/><br/>10.3 Residuals for Detecting Cell-Specific Lack of Fit, 385<br/><br/>10.4 Modeling Ordinal Associations, 386<br/><br/>10.5 Generalized Loglinear and Association Models, Correlation Models, and Correspondence Analysis, 393<br/><br/>10.6 Empty Cells and Sparseness in Modeling Contingency Tables, 398<br/><br/>10.7 Bayesian Loglinear Modeling, 401<br/><br/>Notes, 404<br/><br/>Exercises, 407<br/><br/>11 Models for Matched Pairs 413<br/><br/>11.1 Comparing Dependent Proportions, 414<br/><br/>11.2 Conditional Logistic Regression for Binary Matched Pairs, 418<br/><br/>11.3 Marginal Models for Square Contingency Tables, 424<br/><br/>11.4 Symmetry, Quasi-Symmetry, and Quasi-Independence, 426<br/><br/>11.5 Measuring Agreement Between Observers, 432<br/><br/>11.6 Bradley–Terry Model for Paired Preferences, 436<br/><br/>11.7 Marginal Models and Quasi-Symmetry Models for Matched Sets, 439<br/><br/>Notes, 443<br/><br/>Exercises, 445<br/><br/>12 Clustered Categorical Data: Marginal and Transitional Models 455<br/><br/>12.1 Marginal Modeling: Maximum Likelihood Approach, 456<br/><br/>12.2 Marginal Modeling: Generalized Estimating Equations (GEEs) Approach, 462<br/><br/>12.3 Quasi-Likelihood and Its GEE Multivariate Extension: Details, 465<br/><br/>12.4 Transitional Models: Markov Chain and Time Series Models, 473<br/><br/>Notes, 478<br/><br/>Exercises, 479<br/><br/>13 Clustered Categorical Data: Random Effects Models 489<br/><br/>13.1 Random Effects Modeling of Clustered Categorical Data, 489<br/><br/>13.2 Binary Responses: Logistic-Normal Model, 494<br/><br/>13.3 Examples of Random Effects Models for Binary Data, 498<br/><br/>13.4 Random Effects Models for Multinomial Data, 511<br/><br/>13.5 Multilevel Modeling, 515<br/><br/>13.6 GLMM Fitting, Inference, and Prediction, 519<br/><br/>13.7 Bayesian Multivariate Categorical Modeling, 523<br/><br/>Notes, 525<br/><br/>Exercises, 527<br/><br/>14 Other Mixture Models for Discrete Data 535<br/><br/>14.1 Latent Class Models, 535<br/><br/>14.2 Nonparametric Random Effects Models, 542<br/><br/>14.3 Beta-Binomial Models, 548<br/><br/>14.4 Negative Binomial Regression, 552<br/><br/>14.5 Poisson Regression with Random Effects, 555<br/><br/>Notes, 557<br/><br/>Exercises, 558<br/><br/>15 Non-Model-Based Classification and Clustering 565<br/><br/>15.1 Classification: Linear Discriminant Analysis, 565<br/><br/>15.2 Classification: Tree-Structured Prediction, 570<br/><br/>15.3 Cluster Analysis for Categorical Data, 576<br/><br/>Notes, 581<br/><br/>Exercises, 582<br/><br/>16 Large- and Small-Sample Theory for Multinomial Models 587<br/><br/>16.1 Delta Method, 587<br/><br/>16.2 Asymptotic Distributions of Estimators of Model Parameters and Cell Probabilities, 592<br/><br/>16.3 Asymptotic Distributions of Residuals and Goodness-of-fit Statistics, 594<br/><br/>16.4 Asymptotic Distributions for Logit/Loglinear Models, 599<br/><br/>16.5 Small-Sample Significance Tests for Contingency Tables, 601<br/><br/>16.6 Small-Sample Confidence Intervals for Categorical Data, 603<br/><br/>16.7 Alternative Estimation Theory for Parametric Models, 610<br/><br/>Notes, 615<br/><br/>Exercises, 616<br/><br/>17 Historical Tour of Categorical Data Analysis 623<br/><br/>17.1 Pearson–Yule Association Controversy, 623<br/><br/>17.2 R. A. Fisher’s Contributions, 625<br/><br/>17.3 Logistic Regression, 627<br/><br/>17.4 Multiway Contingency Tables and Loglinear Models, 629<br/><br/>17.5 Bayesian Methods for Categorical Data, 633<br/><br/>17.6 A Look Forward, and Backward, 634<br/><br/>Appendix A Statistical Software for Categorical Data Analysis 637<br/><br/>Appendix B Chi-Squared Distribution Values 641<br/><br/>References 643<br/><br/>Author Index 689<br/><br/>Example Index 701<br/><br/>Subject Index 705<br/><br/>Appendix C Software Details for Text Examples (text website) |
