Alan Agresti
Ordinal Categorical Data 2e
Alan Agresti
Ordinal Categorical Data 2e
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Statistical science s first coordinated manual of methods for analyzing ordered categorical data, now fully revised and updated, continues to present applications and case studies in fields as diverse as sociology, public health, ecology, marketing, and pharmacy. Analysis of Ordinal Categorical Data, Second Edition provides an introduction to basic descriptive and inferential methods for categorical data, giving thorough coverage of new developments and recent methods. Special emphasis is placed on interpretation and application of methods including an integrated comparison of the available…mehr
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Statistical science s first coordinated manual of methods for analyzing ordered categorical data, now fully revised and updated, continues to present applications and case studies in fields as diverse as sociology, public health, ecology, marketing, and pharmacy. Analysis of Ordinal Categorical Data, Second Edition provides an introduction to basic descriptive and inferential methods for categorical data, giving thorough coverage of new developments and recent methods. Special emphasis is placed on interpretation and application of methods including an integrated comparison of the available strategies for analyzing ordinal data. Practitioners of statistics in government, industry (particularly pharmaceutical), and academia will want this new edition.
Produktdetails
- Produktdetails
- Wiley Series in Probability and Statistics
- Verlag: Wiley & Sons
- Artikelnr. des Verlages: 14508289000
- 2. Aufl.
- Seitenzahl: 424
- Erscheinungstermin: 19. April 2010
- Englisch
- Abmessung: 240mm x 161mm x 27mm
- Gewicht: 714g
- ISBN-13: 9780470082898
- ISBN-10: 0470082895
- Artikelnr.: 28240631
- Wiley Series in Probability and Statistics
- Verlag: Wiley & Sons
- Artikelnr. des Verlages: 14508289000
- 2. Aufl.
- Seitenzahl: 424
- Erscheinungstermin: 19. April 2010
- Englisch
- Abmessung: 240mm x 161mm x 27mm
- Gewicht: 714g
- ISBN-13: 9780470082898
- ISBN-10: 0470082895
- Artikelnr.: 28240631
ALAN AGRESTI, PhD, is Distinguished Professor Emeritus in the Department of Statistics at the University of Florida and Visiting Professor in the Department of Statistics at Harvard University. A Fellow of the American Statistical Association and the Institute of Mathematical Statistics, Dr. Agresti has published extensively on the topic of categorical data analysis and has presented lectures and short courses on the subject in more than thirty countries. He is the author of Categorical Data Analysis, Second Edition and An Introduction to Categorical Data Analysis, Second Edition, both published by Wiley.
Preface. 1. Introduction. 1.1. Ordinal Categorical Scales. 1.2. Advantages
of Using Ordinal Methods. 1.3. Ordinal Modeling Versus Ordinary Regession
Analysis. 1.4. Organization of This Book. 2. Ordinal Probabilities, Scores,
and Odds Ratios. 2.1. Probabilities and Scores for an Ordered Categorical
Scale. 2.2. Ordinal Odds Ratios for Contingency Tables. 2.3. Confidence
Intervals for Ordinal Association Measures. 2.4. Conditional Association in
Three-Way Tables. 2.5. Category Choice for Ordinal Variables. Chapter
Notes. Exercises. 3. Logistic Regression Models Using Cumulative Logits.
3.1. Types of Logits for An Ordinal Response. 3.2. Cumulative Logit Models.
3.3. Proportional Odds Models: Properties and Interpretations. 3.4. Fitting
and Inference for Cumulative Logit Models. 3.5. Checking Cumulative Logit
Models. 3.6. Cumulative Logit Models Without Proportional Odds. 3.7.
Connections with Nonparametric Rank Methods. Chapter Notes. Exercises. 4.
Other Ordinal Logistic Regression Models. 4.1. Adjacent-Categories Logit
Models. 4.2. Continuation-Ratio Logit Models. 4.3. Stereotype Model:
Multiplicative Paired-Category Logits. Chapter Notes. Exercises. 5. Other
Ordinal Multinomial Response Models. 5.1. Cumulative Link Models. 5.2.
Cumulative Probit Models. 5.3. Cumulative Log-Log Links: Proportional
Hazards Modeling. 5.4. Modeling Location and Dispersion Effects. 5.5.
Ordinal ROC Curve Estimation. 5.6. Mean Response Models. Chapter Notes.
Exercises. 6. Modeling Ordinal Association Structure. 6.1. Ordinary
Loglinear Modeling. 6.2. Loglinear Model of Linear-by-Linear Association.
6.3. Row or Column Effects Association Models. 6.4. Association Models for
Multiway Tables. 6.5. Multiplicative Association and Correlation Models.
6.6. Modeling Global Odds Ratios and Other Associations. Chapter Notes.
Exercises. 7. Non-Model-Based Analysis of Ordinal Association. 7.1.
Concordance and Discordance Measures of Association. 7.2. Correlation
Measures for Contingency Tables. 7.3. Non-Model-Based Inference for Ordinal
Association Measures. 7.4. Comparing Singly Ordered Multinomials. 7.5.
Order-Restricted Inference with Inequality Constraints. 7.6. Small-Sample
Ordinal Tests of Independence. 7.7. Other Rank-Based Statistical Methods
for Ordered Categories. Appendix: Standard Errors for Ordinal Measures.
Chapter Notes. Exercises. 8. Matched-Pairs Data with Ordered Categories.
8.1. Comparing Marginal Distributions for Matched Pairs. 8.2. Models
Comparing Matched Marginal Distributions. 8.3. Models for The Joint
Distribution in A Square Table. 8.4. Comparing Marginal Distributions for
Matched Sets. 8.5. Analyzing Rater Agreement on an Ordinal Scale. 8.6.
Modeling Ordinal Paired Preferences. Chapter Notes. Exercises. 9. Clustered
Ordinal Responses: Marginal Models. 9.1. Marginal Ordinal Modeling with
Explanatory Variables. 9.2. Marginal Ordinal Modeling: GEE Methods. 9.3.
Transitional Ordinal Modeling, Given the Past. Chapter Notes. Exercises.
10. Clustered Ordinal Responses: Random Effects Models. 10.1. Ordinal
Generalized Linear Mixed Models. 10.2. Examples of Ordinal Random Intercept
Models. 10.3. Models with Multiple Random Effects. 10.4. Multilevel
(Hierarchical) Ordinal Models. 10.5. Comparing Random Effects Models and
Marginal Models. Chapter Notes. Exercises. 11. Bayesian Inference for
Ordinal Response Data. 11.1. Bayesian Approach to Statistical Inference.
11.2. Estimating Multinomial Parameters. 11.3. Bayesian Ordinal Regression
Modeling. 11.4. Bayesian Ordinal Association Modeling. 11.5. Bayesian
Ordinal Multivariate Regression Modeling. 11.6. Bayesian Versus Frequentist
Approaches to Analyzing Ordinal Data. Chapter Notes. Exercises. Appendix
Software for Analyzing Ordinal Categorical Data. Bibliography. Example
Index. Subject Index.
of Using Ordinal Methods. 1.3. Ordinal Modeling Versus Ordinary Regession
Analysis. 1.4. Organization of This Book. 2. Ordinal Probabilities, Scores,
and Odds Ratios. 2.1. Probabilities and Scores for an Ordered Categorical
Scale. 2.2. Ordinal Odds Ratios for Contingency Tables. 2.3. Confidence
Intervals for Ordinal Association Measures. 2.4. Conditional Association in
Three-Way Tables. 2.5. Category Choice for Ordinal Variables. Chapter
Notes. Exercises. 3. Logistic Regression Models Using Cumulative Logits.
3.1. Types of Logits for An Ordinal Response. 3.2. Cumulative Logit Models.
3.3. Proportional Odds Models: Properties and Interpretations. 3.4. Fitting
and Inference for Cumulative Logit Models. 3.5. Checking Cumulative Logit
Models. 3.6. Cumulative Logit Models Without Proportional Odds. 3.7.
Connections with Nonparametric Rank Methods. Chapter Notes. Exercises. 4.
Other Ordinal Logistic Regression Models. 4.1. Adjacent-Categories Logit
Models. 4.2. Continuation-Ratio Logit Models. 4.3. Stereotype Model:
Multiplicative Paired-Category Logits. Chapter Notes. Exercises. 5. Other
Ordinal Multinomial Response Models. 5.1. Cumulative Link Models. 5.2.
Cumulative Probit Models. 5.3. Cumulative Log-Log Links: Proportional
Hazards Modeling. 5.4. Modeling Location and Dispersion Effects. 5.5.
Ordinal ROC Curve Estimation. 5.6. Mean Response Models. Chapter Notes.
Exercises. 6. Modeling Ordinal Association Structure. 6.1. Ordinary
Loglinear Modeling. 6.2. Loglinear Model of Linear-by-Linear Association.
6.3. Row or Column Effects Association Models. 6.4. Association Models for
Multiway Tables. 6.5. Multiplicative Association and Correlation Models.
6.6. Modeling Global Odds Ratios and Other Associations. Chapter Notes.
Exercises. 7. Non-Model-Based Analysis of Ordinal Association. 7.1.
Concordance and Discordance Measures of Association. 7.2. Correlation
Measures for Contingency Tables. 7.3. Non-Model-Based Inference for Ordinal
Association Measures. 7.4. Comparing Singly Ordered Multinomials. 7.5.
Order-Restricted Inference with Inequality Constraints. 7.6. Small-Sample
Ordinal Tests of Independence. 7.7. Other Rank-Based Statistical Methods
for Ordered Categories. Appendix: Standard Errors for Ordinal Measures.
Chapter Notes. Exercises. 8. Matched-Pairs Data with Ordered Categories.
8.1. Comparing Marginal Distributions for Matched Pairs. 8.2. Models
Comparing Matched Marginal Distributions. 8.3. Models for The Joint
Distribution in A Square Table. 8.4. Comparing Marginal Distributions for
Matched Sets. 8.5. Analyzing Rater Agreement on an Ordinal Scale. 8.6.
Modeling Ordinal Paired Preferences. Chapter Notes. Exercises. 9. Clustered
Ordinal Responses: Marginal Models. 9.1. Marginal Ordinal Modeling with
Explanatory Variables. 9.2. Marginal Ordinal Modeling: GEE Methods. 9.3.
Transitional Ordinal Modeling, Given the Past. Chapter Notes. Exercises.
10. Clustered Ordinal Responses: Random Effects Models. 10.1. Ordinal
Generalized Linear Mixed Models. 10.2. Examples of Ordinal Random Intercept
Models. 10.3. Models with Multiple Random Effects. 10.4. Multilevel
(Hierarchical) Ordinal Models. 10.5. Comparing Random Effects Models and
Marginal Models. Chapter Notes. Exercises. 11. Bayesian Inference for
Ordinal Response Data. 11.1. Bayesian Approach to Statistical Inference.
11.2. Estimating Multinomial Parameters. 11.3. Bayesian Ordinal Regression
Modeling. 11.4. Bayesian Ordinal Association Modeling. 11.5. Bayesian
Ordinal Multivariate Regression Modeling. 11.6. Bayesian Versus Frequentist
Approaches to Analyzing Ordinal Data. Chapter Notes. Exercises. Appendix
Software for Analyzing Ordinal Categorical Data. Bibliography. Example
Index. Subject Index.
Preface. 1. Introduction. 1.1. Ordinal Categorical Scales. 1.2. Advantages
of Using Ordinal Methods. 1.3. Ordinal Modeling Versus Ordinary Regession
Analysis. 1.4. Organization of This Book. 2. Ordinal Probabilities, Scores,
and Odds Ratios. 2.1. Probabilities and Scores for an Ordered Categorical
Scale. 2.2. Ordinal Odds Ratios for Contingency Tables. 2.3. Confidence
Intervals for Ordinal Association Measures. 2.4. Conditional Association in
Three-Way Tables. 2.5. Category Choice for Ordinal Variables. Chapter
Notes. Exercises. 3. Logistic Regression Models Using Cumulative Logits.
3.1. Types of Logits for An Ordinal Response. 3.2. Cumulative Logit Models.
3.3. Proportional Odds Models: Properties and Interpretations. 3.4. Fitting
and Inference for Cumulative Logit Models. 3.5. Checking Cumulative Logit
Models. 3.6. Cumulative Logit Models Without Proportional Odds. 3.7.
Connections with Nonparametric Rank Methods. Chapter Notes. Exercises. 4.
Other Ordinal Logistic Regression Models. 4.1. Adjacent-Categories Logit
Models. 4.2. Continuation-Ratio Logit Models. 4.3. Stereotype Model:
Multiplicative Paired-Category Logits. Chapter Notes. Exercises. 5. Other
Ordinal Multinomial Response Models. 5.1. Cumulative Link Models. 5.2.
Cumulative Probit Models. 5.3. Cumulative Log-Log Links: Proportional
Hazards Modeling. 5.4. Modeling Location and Dispersion Effects. 5.5.
Ordinal ROC Curve Estimation. 5.6. Mean Response Models. Chapter Notes.
Exercises. 6. Modeling Ordinal Association Structure. 6.1. Ordinary
Loglinear Modeling. 6.2. Loglinear Model of Linear-by-Linear Association.
6.3. Row or Column Effects Association Models. 6.4. Association Models for
Multiway Tables. 6.5. Multiplicative Association and Correlation Models.
6.6. Modeling Global Odds Ratios and Other Associations. Chapter Notes.
Exercises. 7. Non-Model-Based Analysis of Ordinal Association. 7.1.
Concordance and Discordance Measures of Association. 7.2. Correlation
Measures for Contingency Tables. 7.3. Non-Model-Based Inference for Ordinal
Association Measures. 7.4. Comparing Singly Ordered Multinomials. 7.5.
Order-Restricted Inference with Inequality Constraints. 7.6. Small-Sample
Ordinal Tests of Independence. 7.7. Other Rank-Based Statistical Methods
for Ordered Categories. Appendix: Standard Errors for Ordinal Measures.
Chapter Notes. Exercises. 8. Matched-Pairs Data with Ordered Categories.
8.1. Comparing Marginal Distributions for Matched Pairs. 8.2. Models
Comparing Matched Marginal Distributions. 8.3. Models for The Joint
Distribution in A Square Table. 8.4. Comparing Marginal Distributions for
Matched Sets. 8.5. Analyzing Rater Agreement on an Ordinal Scale. 8.6.
Modeling Ordinal Paired Preferences. Chapter Notes. Exercises. 9. Clustered
Ordinal Responses: Marginal Models. 9.1. Marginal Ordinal Modeling with
Explanatory Variables. 9.2. Marginal Ordinal Modeling: GEE Methods. 9.3.
Transitional Ordinal Modeling, Given the Past. Chapter Notes. Exercises.
10. Clustered Ordinal Responses: Random Effects Models. 10.1. Ordinal
Generalized Linear Mixed Models. 10.2. Examples of Ordinal Random Intercept
Models. 10.3. Models with Multiple Random Effects. 10.4. Multilevel
(Hierarchical) Ordinal Models. 10.5. Comparing Random Effects Models and
Marginal Models. Chapter Notes. Exercises. 11. Bayesian Inference for
Ordinal Response Data. 11.1. Bayesian Approach to Statistical Inference.
11.2. Estimating Multinomial Parameters. 11.3. Bayesian Ordinal Regression
Modeling. 11.4. Bayesian Ordinal Association Modeling. 11.5. Bayesian
Ordinal Multivariate Regression Modeling. 11.6. Bayesian Versus Frequentist
Approaches to Analyzing Ordinal Data. Chapter Notes. Exercises. Appendix
Software for Analyzing Ordinal Categorical Data. Bibliography. Example
Index. Subject Index.
of Using Ordinal Methods. 1.3. Ordinal Modeling Versus Ordinary Regession
Analysis. 1.4. Organization of This Book. 2. Ordinal Probabilities, Scores,
and Odds Ratios. 2.1. Probabilities and Scores for an Ordered Categorical
Scale. 2.2. Ordinal Odds Ratios for Contingency Tables. 2.3. Confidence
Intervals for Ordinal Association Measures. 2.4. Conditional Association in
Three-Way Tables. 2.5. Category Choice for Ordinal Variables. Chapter
Notes. Exercises. 3. Logistic Regression Models Using Cumulative Logits.
3.1. Types of Logits for An Ordinal Response. 3.2. Cumulative Logit Models.
3.3. Proportional Odds Models: Properties and Interpretations. 3.4. Fitting
and Inference for Cumulative Logit Models. 3.5. Checking Cumulative Logit
Models. 3.6. Cumulative Logit Models Without Proportional Odds. 3.7.
Connections with Nonparametric Rank Methods. Chapter Notes. Exercises. 4.
Other Ordinal Logistic Regression Models. 4.1. Adjacent-Categories Logit
Models. 4.2. Continuation-Ratio Logit Models. 4.3. Stereotype Model:
Multiplicative Paired-Category Logits. Chapter Notes. Exercises. 5. Other
Ordinal Multinomial Response Models. 5.1. Cumulative Link Models. 5.2.
Cumulative Probit Models. 5.3. Cumulative Log-Log Links: Proportional
Hazards Modeling. 5.4. Modeling Location and Dispersion Effects. 5.5.
Ordinal ROC Curve Estimation. 5.6. Mean Response Models. Chapter Notes.
Exercises. 6. Modeling Ordinal Association Structure. 6.1. Ordinary
Loglinear Modeling. 6.2. Loglinear Model of Linear-by-Linear Association.
6.3. Row or Column Effects Association Models. 6.4. Association Models for
Multiway Tables. 6.5. Multiplicative Association and Correlation Models.
6.6. Modeling Global Odds Ratios and Other Associations. Chapter Notes.
Exercises. 7. Non-Model-Based Analysis of Ordinal Association. 7.1.
Concordance and Discordance Measures of Association. 7.2. Correlation
Measures for Contingency Tables. 7.3. Non-Model-Based Inference for Ordinal
Association Measures. 7.4. Comparing Singly Ordered Multinomials. 7.5.
Order-Restricted Inference with Inequality Constraints. 7.6. Small-Sample
Ordinal Tests of Independence. 7.7. Other Rank-Based Statistical Methods
for Ordered Categories. Appendix: Standard Errors for Ordinal Measures.
Chapter Notes. Exercises. 8. Matched-Pairs Data with Ordered Categories.
8.1. Comparing Marginal Distributions for Matched Pairs. 8.2. Models
Comparing Matched Marginal Distributions. 8.3. Models for The Joint
Distribution in A Square Table. 8.4. Comparing Marginal Distributions for
Matched Sets. 8.5. Analyzing Rater Agreement on an Ordinal Scale. 8.6.
Modeling Ordinal Paired Preferences. Chapter Notes. Exercises. 9. Clustered
Ordinal Responses: Marginal Models. 9.1. Marginal Ordinal Modeling with
Explanatory Variables. 9.2. Marginal Ordinal Modeling: GEE Methods. 9.3.
Transitional Ordinal Modeling, Given the Past. Chapter Notes. Exercises.
10. Clustered Ordinal Responses: Random Effects Models. 10.1. Ordinal
Generalized Linear Mixed Models. 10.2. Examples of Ordinal Random Intercept
Models. 10.3. Models with Multiple Random Effects. 10.4. Multilevel
(Hierarchical) Ordinal Models. 10.5. Comparing Random Effects Models and
Marginal Models. Chapter Notes. Exercises. 11. Bayesian Inference for
Ordinal Response Data. 11.1. Bayesian Approach to Statistical Inference.
11.2. Estimating Multinomial Parameters. 11.3. Bayesian Ordinal Regression
Modeling. 11.4. Bayesian Ordinal Association Modeling. 11.5. Bayesian
Ordinal Multivariate Regression Modeling. 11.6. Bayesian Versus Frequentist
Approaches to Analyzing Ordinal Data. Chapter Notes. Exercises. Appendix
Software for Analyzing Ordinal Categorical Data. Bibliography. Example
Index. Subject Index.