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Categorical data analysis Alan Agresti

By: Agresti, AlanMaterial type: TextTextLanguage: English Series: Wiley series in probability and statisticsPublication details: Hoboken, NJ Wiley-Interscience 2002 Edition: 2. edDescription: XV, 710 S. graph. Darst. 24 cmISBN: 0471360937 (hbk.); 9780471360933 (hbk.)Subject(s): Multivariate Analyse | Verallgemeinertes lineares Modell | Logit-Modell | Kontingenztafelanalyse | Qualitative Daten | Kategoriale DatenDDC classification: 519.535 LOC classification: QA278Other classification: 31.73 | CM 4000 | SK 830 | MR 2100 | SK 840 | WC 7000 | QH 234 | mat Online resources: Publisher description | Table of contents | Contributor biographical information | Inhaltsverzeichnis | Inhaltstext
Contents:
Contents: Preface. - 1. Introduction: Distributions and Inference for Categorical Data. - 1.1 Categorical Response Data. - 1.2 Distributions for Categorical Data. - 1.3 Statistical Inference for Categorical Data. - 1.4 Statistical Inference for Binomial Parameters. - 1.5 Statistical Inference for Multinomial Parameters. - Notes. - Problems. - 2. Describing Contingency Tables. - 2.1 Probability Structure for Contingency Tables. - 2.2 Comparing Two Proportions. - 2.3 Partial Association in Stratified 2 x 2 Tables. - 2.4 Extensions for I x J Tables. - Notes. - Problems. - 3. Inference for Contingency Tables. - 3.1 Confidence Intervals for Association Parameters. - 3.2 Testing Independence in Two-Way Contingency Tables. - 3.3 Following-Up Chi-Squared Tests. - 3.4 Two-Way Tables with Ordered Classifications. - 3.5 Small-Sample Tests of Independence. - 3.6 Small-Sample Confidence Intervals for 2 x 2 Tables. - 3.7 Extensions for Multiway Tables and Nontabulated Responses. - Notes. - Problems. - 4. Introduction to Generalized Linear Models. - 4.1 Generalized Linear Model. - 4.2 Generalized Linear Models for Binary Data. - 4.3 Generalized Linear Models for Counts. - 4.4 Moments and Likelihood for Generalized Linear Models. - 4.5 Inference for Generalized Linear Models. - 4.6 Fitting Generalized Linear Models. - 4.7 Quasi-likelihood and Generalized Linear Models. - 4.8 Generalized Additive Models. - Notes. - Problems. - 5. Logistic Regression. - 5.1 Interpreting Parameters in Logistic Regression. - 5.2 Inference for Logistic Regression. - 5.3 Logit Models with Categorical Predictors. - 5.4 Multiple Logistic Regression. - 5.5 Fitting Logistic Regression Models. - Notes. - Problems. - 6. Building and Applying Logistic Regression Models. - 6.1 Strategies in Model Selection. - 6.2 Logistic Regression Diagnostics. - 6.3 Inference About Conditional Associations in 2 x 2 x K Tables. - 6.4 Using Models to Improve Inferential Power. - 6.5 Sample Size and Power Considerations. - 6.6 Probit and Complementary Log-Log Models. - 6.7 Conditional Logistic Regression and Exact Distributions. - Notes. - Problems. - 7. Logit Models for Multinomial Responses. - 7.1 Nominal Responses: Baseline-Category Logit Models. - 7.2 Ordinal Responses: Cumulative Logit Models. - 7.3 Ordinal Responses: Cumulative Link Models. - 7.4 Alternative Models for Ordinal Responses. - 7.5 Testing Conditional Independence in I x J x K Tables. - 7.6 Discrete-Choice Multinomial Logit Models. - Notes. - Problems. - 8. Loglinear Models for Contingency Tables. - 8.1 Loglinear Models for Two-Way Tables. - 8.2 Loglinear Models for Independence and Interaction in Three-Way Tables. - 8.3 Inference for Loglinear Models. - 8.4 Loglinear Models for Higher Dimensions. - 8.5 The Loglinear_Logit Model Connection. - 8.6 Loglinear Model Fitting: Likelihood Equations and Asymptotic Distributions. - 8.7 Loglinear Model Fitting: Iterative Methods and their Application. - Notes. - Problems. - 9. Building and Extending Loglinear/Logit Models. - 9.1 Association Graphs and Collapsibility. - 9.2 Model Selection and Comparison. - 9.3 Diagnostics for Checking Models. - 9.4 Modeling Ordinal Associations. - 9.5 Association Models. - 9.6 Association Models, Correlation Models, and Correspondence Analysis. - 9.7 Poisson Regression for Rates. - 9.8 Empty Cells and Sparseness in Modeling Contingency Tables. - Notes. - Problems. - 10. Models for Matched Pairs. - 10.1 Comparing Dependent Proportions. - 10.2 Conditional Logistic Regression for Binary Matched Pairs. - 10.3 Marginal Models for Square Contingency Tables. - 10.4 Symmetry, Quasi-symmetry, and Quasiindependence. - 10.5 Measuring Agreement Between Observers. - 10.6 Bradley-Terry Model for Paired Preferences. - 10.7 Marginal Models and Quasi-symmetry Models for Matched Sets. - Notes. - Problems. - 11. Analyzing Repeated Categorical Response Data. - 11.1 Comparing Marginal Distributions: Multiple Responses. - 11.2 Marginal Modeling: Maximum Likelihood Approach. - 11.3 Marginal Modeling: Generalized Estimating Equations Approach. - 11.4 Quasi-likelihood and Its GEE Multivariate Extension: Details. - 11.5 Markov Chains: Transitional Modeling. - Notes. - Problems. - 12. Random Effects: Generalized Linear Mixed Models for Categorical Responses. - 12.1 Random Effects Modeling of Clustered Categorical Data. - 12.2 Binary Responses: Logistic-Normal Model. - 12.3 Examples of Random Effects Models for Binary Data. - 12.4 Random Effects Models for Multinomial Data. - 12.5 Multivariate Random Effects Models for Binary Data. - 12.6 GLMM Fitting, Inference, and Prediction. - Notes. - Problems. 13. Other Mixture Models for Categorical Data. - 13.1 Latent Class Models. - 13.2 Nonparametric Random Effects Models. - 13.3 Beta-Binomial Models. - 13.4 Negative Binomial Regression. - 13.5 Poisson Regression with Random Effects. - Notes. - Problems. - 14. Asymptotic Theory for Parametric Models. - 14.1 Delta Method. - 14.2 Asymptotic Distributions of Estimators of Model Parameters and Cell Probabilities. - 14.3 Asymptotic Distributions of Residuals and Goodnessof-Fit Statistics. - 14.4 Asymptotic Distributions for Logit/Loglinear Models. - Notes. - Problems. - 15. Alternative Estimation Theory for Parametric Models. - 15.1 Weighted Least Squares for Categorical Data. - 15.2 Bayesian Inference for Categorical Data. - 15.3 Other Methods of Estimation. - Notes. - Problems. - 16. Historical Tour of Categorical Data Analysis. - 16.1 Pearson-Yule Association Controversy. - 16.2 R. A. Fisher's Contributions. - 16.3 Logistic Regression. - 16.4 Multiway Contingency Tables and Loglinear Models. - 16.5 Recent and Future? Developments. - Appendix A. Using Computer Software to Analyze Categorical Data. - A.1 Software for Categorical Data Analysis. - A.2 Examples of SAS Code by Chapter. - Appendix B. Chi-Squared Distribution Values. - References. - Examples Index. - Author Index. - Subject Index.
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Literaturverz. S. 655 - 688

Contents:
Preface. -
1. Introduction: Distributions and Inference for Categorical Data. -
1.1 Categorical Response Data. -
1.2 Distributions for Categorical Data. -
1.3 Statistical Inference for Categorical Data. -
1.4 Statistical Inference for Binomial Parameters. -
1.5 Statistical Inference for Multinomial Parameters. -
Notes. -
Problems. -
2. Describing Contingency Tables. -
2.1 Probability Structure for Contingency Tables. -
2.2 Comparing Two Proportions. -
2.3 Partial Association in Stratified 2 x 2 Tables. -
2.4 Extensions for I x J Tables. -
Notes. -
Problems. -
3. Inference for Contingency Tables. -
3.1 Confidence Intervals for Association Parameters. -
3.2 Testing Independence in Two-Way Contingency Tables. -
3.3 Following-Up Chi-Squared Tests. -
3.4 Two-Way Tables with Ordered Classifications. -
3.5 Small-Sample Tests of Independence. -
3.6 Small-Sample Confidence Intervals for 2 x 2 Tables. -
3.7 Extensions for Multiway Tables and Nontabulated Responses. -
Notes. -
Problems. -
4. Introduction to Generalized Linear Models. -
4.1 Generalized Linear Model. -
4.2 Generalized Linear Models for Binary Data. -
4.3 Generalized Linear Models for Counts. -
4.4 Moments and Likelihood for Generalized Linear Models. -
4.5 Inference for Generalized Linear Models. -
4.6 Fitting Generalized Linear Models. -
4.7 Quasi-likelihood and Generalized Linear Models. -
4.8 Generalized Additive Models. -
Notes. -
Problems. -
5. Logistic Regression. -
5.1 Interpreting Parameters in Logistic Regression. -
5.2 Inference for Logistic Regression. -
5.3 Logit Models with Categorical Predictors. -
5.4 Multiple Logistic Regression. -
5.5 Fitting Logistic Regression Models. -
Notes. -
Problems. -
6. Building and Applying Logistic Regression Models. -
6.1 Strategies in Model Selection. -
6.2 Logistic Regression Diagnostics. -
6.3 Inference About Conditional Associations in 2 x 2 x K Tables. -
6.4 Using Models to Improve Inferential Power. -
6.5 Sample Size and Power Considerations. -
6.6 Probit and Complementary Log-Log Models. -
6.7 Conditional Logistic Regression and Exact Distributions. -
Notes. -
Problems. -
7. Logit Models for Multinomial Responses. -
7.1 Nominal Responses: Baseline-Category Logit Models. -
7.2 Ordinal Responses: Cumulative Logit Models. -
7.3 Ordinal Responses: Cumulative Link Models. -
7.4 Alternative Models for Ordinal Responses. -
7.5 Testing Conditional Independence in I x J x K Tables. -
7.6 Discrete-Choice Multinomial Logit Models. -
Notes. -
Problems. -
8. Loglinear Models for Contingency Tables. -
8.1 Loglinear Models for Two-Way Tables. -
8.2 Loglinear Models for Independence and Interaction in Three-Way Tables. -
8.3 Inference for Loglinear Models. -
8.4 Loglinear Models for Higher Dimensions. -
8.5 The Loglinear_Logit Model Connection. -
8.6 Loglinear Model Fitting: Likelihood Equations and Asymptotic Distributions. -
8.7 Loglinear Model Fitting: Iterative Methods and their Application. -
Notes. -
Problems. -
9. Building and Extending Loglinear/Logit Models. -
9.1 Association Graphs and Collapsibility. -
9.2 Model Selection and Comparison. -
9.3 Diagnostics for Checking Models. -
9.4 Modeling Ordinal Associations. -
9.5 Association Models. -
9.6 Association Models, Correlation Models, and Correspondence Analysis. -
9.7 Poisson Regression for Rates. -
9.8 Empty Cells and Sparseness in Modeling Contingency Tables. -
Notes. -
Problems. -
10. Models for Matched Pairs. -
10.1 Comparing Dependent Proportions. -
10.2 Conditional Logistic Regression for Binary Matched Pairs. -
10.3 Marginal Models for Square Contingency Tables. -
10.4 Symmetry, Quasi-symmetry, and Quasiindependence. -
10.5 Measuring Agreement Between Observers. -
10.6 Bradley-Terry Model for Paired Preferences. -
10.7 Marginal Models and Quasi-symmetry Models for Matched Sets. -
Notes. -
Problems. -
11. Analyzing Repeated Categorical Response Data. -
11.1 Comparing Marginal Distributions: Multiple Responses. -
11.2 Marginal Modeling: Maximum Likelihood Approach. -
11.3 Marginal Modeling: Generalized Estimating Equations Approach. -
11.4 Quasi-likelihood and Its GEE Multivariate Extension: Details. -
11.5 Markov Chains: Transitional Modeling. -
Notes. -
Problems. -
12. Random Effects: Generalized Linear Mixed Models for Categorical Responses. -
12.1 Random Effects Modeling of Clustered Categorical Data. -
12.2 Binary Responses: Logistic-Normal Model. -
12.3 Examples of Random Effects Models for Binary Data. -
12.4 Random Effects Models for Multinomial Data. -
12.5 Multivariate Random Effects Models for Binary Data. -
12.6 GLMM Fitting, Inference, and Prediction. -
Notes. -
Problems.
13. Other Mixture Models for Categorical Data. -
13.1 Latent Class Models. -
13.2 Nonparametric Random Effects Models. -
13.3 Beta-Binomial Models. -
13.4 Negative Binomial Regression. -
13.5 Poisson Regression with Random Effects. -
Notes. -
Problems. -
14. Asymptotic Theory for Parametric Models. -
14.1 Delta Method. -
14.2 Asymptotic Distributions of Estimators of Model Parameters and Cell Probabilities. -
14.3 Asymptotic Distributions of Residuals and Goodnessof-Fit Statistics. -
14.4 Asymptotic Distributions for Logit/Loglinear Models. -
Notes. -
Problems. -
15. Alternative Estimation Theory for Parametric Models. -
15.1 Weighted Least Squares for Categorical Data. -
15.2 Bayesian Inference for Categorical Data. -
15.3 Other Methods of Estimation. -
Notes. -
Problems. -
16. Historical Tour of Categorical Data Analysis. -
16.1 Pearson-Yule Association Controversy. -
16.2 R. A. Fisher's Contributions. -
16.3 Logistic Regression. -
16.4 Multiway Contingency Tables and Loglinear Models. -
16.5 Recent and Future? Developments. -
Appendix A. Using Computer Software to Analyze Categorical Data. -
A.1 Software for Categorical Data Analysis. -
A.2 Examples of SAS Code by Chapter. -
Appendix B. Chi-Squared Distribution Values. -
References. -
Examples Index. -
Author Index. -
Subject Index.

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