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Produktbild: Categorical Data Analysis

Categorical Data Analysis

Aus der Reihe Wiley Series in Probability and Statistics

182,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

26.11.2012

Verlag

John Wiley & Sons

Seitenzahl

752

Maße (L/B/H)

26/18,3/4,4 cm

Gewicht

1429 g

Auflage

3. Auflage

Sprache

Englisch

ISBN

978-0-470-46363-5

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

26.11.2012

Verlag

John Wiley & Sons

Seitenzahl

752

Maße (L/B/H)

26/18,3/4,4 cm

Gewicht

1429 g

Auflage

3. Auflage

Sprache

Englisch

ISBN

978-0-470-46363-5

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  • Produktbild: Categorical Data Analysis

  • 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, 8

    1.4 Statistical Inference for Binomial Parameters, 13

    1.5 Statistical Inference for Multinomial Parameters, 17

    1.6 Bayesian Inference for Binomial and Multinomial Parameters, 22

    Notes, 27

    Exercises, 28

    2 Describing Contingency Tables 37

    2.1 Probability Structure for Contingency Tables, 37

    2.2 Comparing Two Proportions, 43

    2.3 Conditional Association in Stratified 2 × 2 Tables, 47

    2.4 Measuring Association in I × J Tables, 54

    Notes, 60

    Exercises, 60

    3 Inference for Two-Way Contingency Tables 69

    3.1 Confidence Intervals for Association Parameters, 69

    3.2 Testing Independence in Two-way Contingency Tables, 75

    3.3 Following-up Chi-Squared Tests, 80

    3.4 Two-Way Tables with Ordered Classifications, 86

    3.5 Small-Sample Inference for Contingency Tables, 90

    3.6 Bayesian Inference for Two-way Contingency Tables, 96

    3.7 Extensions for Multiway Tables and Nontabulated Responses, 100

    Notes, 101

    Exercises, 103

    4 Introduction to Generalized Linear Models 113

    4.1 The Generalized Linear Model, 113

    4.2 Generalized Linear Models for Binary Data, 117

    4.3 Generalized Linear Models for Counts and Rates, 122

    4.4 Moments and Likelihood for Generalized Linear Models, 130

    4.5 Inference and Model Checking for Generalized Linear Models, 136

    4.6 Fitting Generalized Linear Models, 143

    4.7 Quasi-Likelihood and Generalized Linear Models, 149

    Notes, 152

    Exercises, 153

    5 Logistic Regression 163

    5.1 Interpreting Parameters in Logistic Regression, 163

    5.2 Inference for Logistic Regression, 169

    5.3 Logistic Models with Categorical Predictors, 175

    5.4 Multiple Logistic Regression, 182

    5.5 Fitting Logistic Regression Models, 192

    Notes, 195

    Exercises, 196

    6 Building, Checking, and Applying Logistic Regression Models 207

    6.1 Strategies in Model Selection, 207

    6.2 Logistic Regression Diagnostics, 215

    6.3 Summarizing the Predictive Power of a Model, 221

    6.4 Mantel-Haenszel and Related Methods for Multiple 2 × 2 Tables, 225

    6.5 Detecting and Dealing with Infinite Estimates, 233

    6.6 Sample Size and Power Considerations, 237

    Notes, 241

    Exercises, 243

    7 Alternative Modeling of Binary Response Data 251

    7.1 Probit and Complementary Log-log Models, 251

    7.2 Bayesian Inference for Binary Regression, 257

    7.3 Conditional Logistic Regression, 265

    7.4 Smoothing: Kernels, Penalized Likelihood, Generalized Additive Models, 270

    7.5 Issues in Analyzing High-Dimensional Categorical Data, 278

    Notes, 285

    Exercises, 287

    8 Models for Multinomial Responses 293

    8.1 Nominal Responses: Baseline-Category Logit Models, 293

    8.2 Ordinal Responses: Cumulative Logit Models, 301

    8.3 Ordinal Responses: Alternative Models, 308

    8.4 Testing Conditional Independence in I × J × K Tables, 314

    8.5 Discrete-Choice Models, 320

    8.6 Bayesian Modeling of Multinomial Responses, 323

    Notes, 326

    Exercises, 329

    9 Loglinear Models for Contingency Tables 339

    9.1 Loglinear Models for Two-way Tables, 339

    9.2 Loglinear Models for Independence and Interaction in Three-way Tables, 342

    9.3 Inference for Loglinear Models, 348

    9.4 Loglinear Models for Higher Dimensions, 350

    9.5 Loglinear-Logistic Model Connection, 353

    9.6 Loglinear Model Fitting: Likelihood Equations and Asymptotic Distributions, 356

    9.7 Loglinear Model Fitting: Iterative Methods and Their Application, 364

    Notes, 368

    Exercises, 369

    10 Building and Extending Loglinear Models 377

    10.1 Conditional Independence Graphs and Collapsibility, 377

    10.2 Model Selection and Comparison, 380

    10.3 Residuals for Detecting Cell-Specific Lack of Fit, 385

    10.4 Modeling Ordinal Associations, 386

    10.5 Generalized Loglinear and Association Models, Correlation Models, and Correspondence Analysis, 393

    10.6 Empty Cells and Sparseness in Modeling Contingency Tables, 398

    10.7 Bayesian Loglinear Modeling, 401

    Notes, 404

    Exercises, 407

    11 Models for Matched Pairs 413

    11.1 Comparing Dependent Proportions, 414

    11.2 Conditional Logistic Regression for Binary Matched Pairs, 418

    11.3 Marginal Models for Square Contingency Tables, 424

    11.4 Symmetry, Quasi-Symmetry, and Quasi-Independence, 426

    11.5 Measuring Agreement Between Observers, 432

    11.6 Bradley-Terry Model for Paired Preferences, 436

    11.7 Marginal Models and Quasi-Symmetry Models for Matched Sets, 439

    Notes, 443

    Exercises, 445

    12 Clustered Categorical Data: Marginal and Transitional Models 455

    12.1 Marginal Modeling: Maximum Likelihood Approach, 456

    12.2 Marginal Modeling: Generalized Estimating Equations (GEEs) Approach, 462

    12.3 Quasi-Likelihood and Its GEE Multivariate Extension: Details, 465

    12.4 Transitional Models: Markov Chain and Time Series Models, 473

    Notes, 478

    Exercises, 479

    13 Clustered Categorical Data: Random Effects Models 489

    13.1 Random Effects Modeling of Clustered Categorical Data, 489

    13.2 Binary Responses: Logistic-Normal Model, 494

    13.3 Examples of Random Effects Models for Binary Data, 498

    13.4 Random Effects Models for Multinomial Data, 511

    13.5 Multilevel Modeling, 515

    13.6 GLMM Fitting, Inference, and Prediction, 519

    13.7 Bayesian Multivariate Categorical Modeling, 523

    Notes, 525

    Exercises, 527

    14 Other Mixture Models for Discrete Data 535

    14.1 Latent Class Models, 535

    14.2 Nonparametric Random Effects Models, 542

    14.3 Beta-Binomial Models, 548

    14.4 Negative Binomial Regression, 552

    14.5 Poisson Regression with Random Effects, 555

    Notes, 557

    Exercises, 558

    15 Non-Model-Based Classification and Clustering 565

    15.1 Classification: Linear Discriminant Analysis, 565

    15.2 Classification: Tree-Structured Prediction, 570

    15.3 Cluster Analysis for Categorical Data, 576

    Notes, 581

    Exercises, 582

    16 Large- and Small-Sample Theory for Multinomial Models 587

    16.1 Delta Method, 587

    16.2 Asymptotic Distributions of Estimators of Model Parameters and Cell Probabilities, 592

    16.3 Asymptotic Distributions of Residuals and Goodness-of-fit Statistics, 594

    16.4 Asymptotic Distributions for Logit/Loglinear Models, 599

    16.5 Small-Sample Significance Tests for Contingency Tables, 601

    16.6 Small-Sample Confidence Intervals for Categorical Data, 603

    16.7 Alternative Estimation Theory for Parametric Models, 610

    Notes, 615

    Exercises, 616

    17 Historical Tour of Categorical Data Analysis 623

    17.1 Pearson-Yule Association Controversy, 623

    17.2 R. A. Fisher's Contributions, 625

    17.3 Logistic Regression, 627

    17.4 Multiway Contingency Tables and Loglinear Models, 629

    17.5 Bayesian Methods for Categorical Data, 633

    17.6 A Look Forward, and Backward, 634

    Appendix A Statistical Software for Categorical Data Analysis 637

    Appendix B Chi-Squared Distribution Values 641

    References 643

    Author Index 689

    Example Index 701

    Subject Index 705

    Appendix C Software Details for Text Examples (text website)