[学科前沿] 下载 Categorial Data Analysis 2nd (Alan Agresti) [推广有奖]

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langaoz 发表于 2007-5-20 09:50:00 |只看作者 |坛友微信交流群|倒序 |AI写论文

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*名称：Categorial Data Analysis
*大小：732页
*格式：PDF
*目录：
Contents
Preface xiii
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
Notes, 26
Problems, 28
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 22 Tables, 47
2.4 Extensions for IJ Tables, 54
Notes, 59
Problems, 60
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 22 Tables,* 98
3.7 Extensions for Multiway Tables and Nontabulated
Responses, 101
Notes, 102
Problems, 104
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
Notes, 155
Problems, 156
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
Notes, 196
Problems, 197
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 22K
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
Notes, 257
Problems, 259
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 IJK
Tables,* 293
7.6 Discrete-Choice Multinomial Logit Models,* 298
Notes, 302
Problems, 302
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 LoglinearLogit 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
Notes, 346
Problems, 347
9. Building and Extending LoglinearrLogit 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
Notes, 398
Problems, 400
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 Quasiindependence,
423
10.5 Measuring Agreement Between Observers, 431
10.6 BradleyTerry Model for Paired Preferences, 436
10.7 Marginal Models and Quasi-symmetry Models for
Matched Sets,* 439
Notes, 442
Problems, 444
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
Notes, 481
Problems, 482
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
Notes, 526
Problems, 527
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
Notes, 565
Problems, 566
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 Goodnessof-
Fit Statistics, 587
14.4 Asymptotic Distributions for LogitrLoglinear
Models, 592
Notes, 594
Problems, 595
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
Notes, 615
Problems, 616
16. Historical Tour of Categorical Data Analysis* 619
16.1 PearsonYule 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 RecentŽand Future?.Developments, 629
Appendix A. Using Computer Software to Analyze Categorical Data 632
A.1 Software for Categorical Data Analysis, 632
A.2 Examples of SAS Code by Chapter, 634
Appendix B. Chi-Squared Distribution Values 654
References 655
Examples Index 689
Author Index 693
Subject Index 701