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Praise for the Second Edition
"A must-have book for anyone expecting to do research and/or applications in categorical data analysis."
--Statistics in Medicine
"It is a total delight reading this book."
--Pharmaceutical Research
"If you do any analysis of categorical data, this is an essential desktop reference."
--Technometrics
The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis.
Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features:
* An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models
* Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis
* New sections introducing the Bayesian approach for methods in that chapter
* More than 100 analyses of data sets and over 600 exercises
* Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources
* A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions
Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.
"A must-have book for anyone expecting to do research and/or applications in categorical data analysis."
--Statistics in Medicine
"It is a total delight reading this book."
--Pharmaceutical Research
"If you do any analysis of categorical data, this is an essential desktop reference."
--Technometrics
The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis.
Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features:
* An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models
* Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis
* New sections introducing the Bayesian approach for methods in that chapter
* More than 100 analyses of data sets and over 600 exercises
* Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources
* A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions
Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.
Praise for the Second Edition
"A must-have book for anyone expecting to do research and/or applications in categorical data analysis."
--Statistics in Medicine
"It is a total delight reading this book."
--Pharmaceutical Research
"If you do any analysis of categorical data, this is an essential desktop reference."
--Technometrics
The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis.
Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features:
* An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models
* Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis
* New sections introducing the Bayesian approach for methods in that chapter
* More than 100 analyses of data sets and over 600 exercises
* Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources
* A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions
Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.
"A must-have book for anyone expecting to do research and/or applications in categorical data analysis."
--Statistics in Medicine
"It is a total delight reading this book."
--Pharmaceutical Research
"If you do any analysis of categorical data, this is an essential desktop reference."
--Technometrics
The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis.
Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features:
* An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models
* Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis
* New sections introducing the Bayesian approach for methods in that chapter
* More than 100 analyses of data sets and over 600 exercises
* Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources
* A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions
Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.
Inhaltsverzeichnis
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
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
Details
| Erscheinungsjahr: | 2013 |
|---|---|
| Fachbereich: | Wahrscheinlichkeitstheorie |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Seiten: | 752 |
| Inhalt: | 752 S. |
| ISBN-13: | 9780470463635 |
| ISBN-10: | 0470463635 |
| Sprache: | Englisch |
| Herstellernummer: | 14546363000 |
| Autor: | Agresti, Alan |
| Auflage: | 3. Aufl. |
| Hersteller: |
Wiley
Wiley & Sons |
| Maße: | 259 x 178 x 42 mm |
| Von/Mit: | Alan Agresti |
| Erscheinungsdatum: | 11.01.2013 |
| Gewicht: | 1,462 kg |
Inhaltsverzeichnis
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
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
Details
| Erscheinungsjahr: | 2013 |
|---|---|
| Fachbereich: | Wahrscheinlichkeitstheorie |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Seiten: | 752 |
| Inhalt: | 752 S. |
| ISBN-13: | 9780470463635 |
| ISBN-10: | 0470463635 |
| Sprache: | Englisch |
| Herstellernummer: | 14546363000 |
| Autor: | Agresti, Alan |
| Auflage: | 3. Aufl. |
| Hersteller: |
Wiley
Wiley & Sons |
| Maße: | 259 x 178 x 42 mm |
| Von/Mit: | Alan Agresti |
| Erscheinungsdatum: | 11.01.2013 |
| Gewicht: | 1,462 kg |
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