Author Agresti, Alan. 
Title Categorical data analysis / Alan Agresti. 
Publisher New York : Wiley-Interscience, c2002.
Edition 2nd ed.

 
 
 
 Preface 
1 Introduction: Distributions and Inference for Categorical Data 1
 1.1 Categorical Response Data 1
 1.2 Distributions for Categorical Data 5
 1.3 Statistical Inference for Categorical Data 9
 1.4 Statistical Inference for Binomial Parameters 14
 1.5 Statistical Inference for Multinomial Parameters 21
2 Describing Contingency Tables 36
 2.1 Probability Structure for 'Contingency Tables 36
 2.2 Comparing Two Proportions 43
 2.3 Partial Association in Stratified 2 X 2 Tables 47
 2.4 Extensions for I X J Tables 54
3 Inference for Contingency Tables 70
 3.1 Confidence Intervals for Association Parameters 70
 3.2 Testing Independence in Two-Way Contingency Tables 78
 3.3 Following-Up Chi-Squared Tests 80
 3.4 Two-Way Tables with Ordered Classifications 86
 3.5 Small-Sample Tests of Independence 91
 3.6 Small-Sample Confidence Intervals for 2 X 2 Tables 98
 3.7 Extensions for Multiway Tables and Nontabulated Responses 101
4 Introduction to Generalized Linear Models 115
 4.1 Generalized Linear Model 116
 4.2 Generalized Linear Models for Binary Data 120
 4.3 Generalized Linear Models for Counts 125
 4.4 Moments and Likelihood for Generalized Linear Models 132
 4.5 Inference for Generalized Linear Models 139
 4.6 Fitting Generalized Linear Models 143
 4.7 Quasi-likelihood and Generalized Linear Models 149
 4.8 Generalized Additive Models 153
5 Logistic Regression 165
 5.1 Interpreting Parameters in Logistic Regression 166
 5.2 Inference for Logistic Regression 172
 5.3 Logit Models with Categorical Predictors 177
 5.4 Multiple Logistic Regression 182
 5.5 Fitting Logistic Regression Models 192
6 Building and Applying Logistic Regression Models 211
 6.1 Strategies in Model Selection 211
 6.2 Logistic Regression Diagnostics 219
 6.3 Inference About Conditional Associations in 2 X 2 X K Tables 230
 6.4 Using Models to Improve Inferential Power 236
 6.5 Sample Size and Power Considerations 240
 6.6 Probit and Complementary Log-Log Models 245
 6.7 Conditional Logistic Regression and Exact Distributions 250
7 Logit Models for Multinomial Responses 267
 7.1 Nominal Responses: Baseline-Category Logit Models 267
 7.2 Ordinal Responses: Cumulative Logit Models 274
 7.3 Ordinal Responses: Cumulative Link Models 282
 7.4 Alternative Models for Ordinal Responses 286
 7.5 Testing Conditional Independence in I X J X K Tables 293
 7.6 Discrete-Choice Multinomial Logit Models 298
8 Loglinear Models for Contingency Tables 314
 8.1 Loglinear Models for Two-Way Tables 314
 8.2 Loglinear Models for Independence and Interaction in Three-Way Tables 318
 8.3 Inference for Loglinear Models 324
 8.4 Loglinear Models for Higher Dimensions 326
 8.5 The Loglinear-Logit Model Connection 330
 8.6 Loglinear Model Fitting: Likelihood Equations and Asymptotic Distributions 333
 8.7 Loglinear Model Fitting: Iterative Methods and their Application 342
9 Building and Extending Loglinear/Logit Models 357
 9.1 Association Graphs and Collapsibility 357
 9.2 Model Selection and Comparison 360
 9.3 Diagnostics for Checking Models 366
 9.4 Modeling Ordinal Associations 367
 9.5 Association Models 373
 9.6 Association Models, Correlation Models, and Correspondence Analysis 379
 9.7 Poisson Regression for Rates 385
 9.8 Empty Cells and Sparseness in Modeling Contingency Tables 391
10 Models for Matched Pairs 409
 10.1 Comparing Dependent Proportions 410
 10.2 Conditional Logistic Regression for Binary Matched Pairs 414
 10.3 Marginal Models for Square Contingency Tables 420
 10.4 Symmetry, Quasi-symmetry, and Quasi-independence 423
 10.5 Measuring Agreement Between Observers 431
 10.6 Bradley-Terry Model for Paired Preferences 436
 10.7 Marginal Models and Quasi-symmetry Models for Matched Sets 439
11 Analyzing Repeated Categorical Response Data 455
 11.1 Comparing Marginal Distributions: Multiple Responses 456
 11.2 Marginal Modeling: Maximum Likelihood Approach 459
 11.3 Marginal Modeling: Generalized Estimating Equations Approach 466
 11.4 Quasi-likelihood and Its GEE Multivariate Extension: Details 470
 11.5 Markov Chains: Transitional Modeling 476
12 Random Effects: Generalized Linear Mixed Models for Categorical Responses 491
 12.1 Random Effects Modeling of Clustered Categorical Data 492
 12.2 Binary Responses: Logistic-Normal Model 496
 12.3 Examples of Random Effects Models for Binary Data 502
 12.4 Random Effects Models for Multinomial Data 513
 12.5 Multivariate Random Effects Models for Binary Data 516
 12.6 GLMM Fitting, Inference, and Prediction 520
13 Other Mixture Models for Categorical Data 538
 13.1 Latent Class Models 538
 13.2 Nonparametric Random Effects Models 545
 13.3 Beta-Binomial Models 553
 13.4 Negative Binomial Regression 559
 13.5 Poisson Regression with Random Effects 563
14 Asymptotic Theory for Parametric Models 576
 14.1 Delta Method 577
 14.2 Asymptotic Distributions of Estimators of Model Parameters and Cell Probabilities 582
 14.3 Asymptotic Distributions of Residuals and Goodness-of-Fit Statistics 587
 14.4 Asymptotic Distributions for Logit/Loglinear Models 592
15 Alternative Estimation Theory for Parametric Models 600
 15.1 Weighted Least Squares for Categorical Data 600
 15.2 Bayesian Inference for Categorical Data 604
 15.3 Other Methods of Estimation 611
16 Historical Tour of Categorical Data Analysis 619
 16.1 Pearson-Yule Association Controversy 619
 16.2 R. A. Fisher's Contributions 622
 16.3 Logistic Regression 624
 16.4 Multiway Contingency Tables and Loglinear Models 625
 16.5 Recent (and Future?) Developments 629
App. A Using Computer Software to Analyze Categorical Data 632
App. B Chi-Squared Distribution Values 654
 References 655
 Examples Index 689
 Author Index 693
 Subject Index
 

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