Cargando…

Categorical data analysis

Detalles Bibliográficos
Autor principal:	Agresti, Alan
Formato:	Libro
Lenguaje:	English
Publicado:	Hoboken, New Jersey : Wiley, ©2013.
Edición:	Third edition.
Colección:	Wiley series in probability and statistics ; 792
Materias:	Análisis multivariante

Tabla de Contenidos:

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
1.6. Bayesian inference for binomial and multinomial parameters notes exercises
2. Describing contingency tables
2.1. Probability structure for contingency tables
2.2. Comparing two proportions
2.3. Conditional association in stratified 2x2 tables
2.4. Measuring association in I x J tables
3. Inference for two-way 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 inference for contingency tables
3.6. Bayesian inference for two-way contingency tables
3.7. Extensions for multiway tables and nontabulated responses
4. Introduction to generalized linear models
4.1. The generalized linear model
4.2. Generalized linear models for binary data
4.3. Generalized linear models for counts and rates
4.4. Moments and likelihood for generalized linear models
4.5. Inference and model checking for generalized linear models
4.6. Fitting generalized linear models
4.7. Quasi-likelihood and generalized linear models
5. Logistic regression
5.1. Interpreting parameters in logistic regression
5.2. Inference for logistic regression
5.3. Logistic models with categorical predictors
5.4. Multiple logistic regression
5.5. Fitting logistic regression models
6. Building, checking, and applying logistic regression models
6.1. Strategies in model selection
6.2. Logistic regression diagnostics
6.3. Summarizing the predictive power of a model
6.4. Mantel-haenszel and related methods for multiple 2x2 tables
6.5. Detecting and dealing with infinite estimates
6.6. Sample size and power considerations
7. Alternative modeling of binary response data
7.1. Probit and complementary log-log models
7.2. Bayesian inference for binary regression
7.3. Conditional logistic regression
7.4. Smoothing: kernels, penalized likelihood, generalized additive models
7.5. Issues in analyzing high-dimensional categorical data
8. Models for multinomial responses
8.1. Nominal responses: baseline-category logit models
8.2. Ordinal responses: cumulative logit models
8.3. Ordinal responses: alternative models
8.4. Testing conditional independence in I x J x K tables
8.5. Discrete-choice models
8.6. Bayesian modeling of multinomial responses
9. Loglinear models for contingency tables
9.1. Loglinear models for two-way tables
9.2. Loglinear models for independence and interaction in three-way tables
9.3. Inference for loglinear models
9.4. Loglinear models for higher dimensions
9.5. The loglinear-logistic model connection
9.6. Loglinear model fitting: likelihood equations and asymptotic distributions
9.7. Loglinear model fitting: iterative methods and their application
10. Building and extending loglinear models
10.1. Conditional independence graphs and collapsibility
10.2. Model selection and comparison
10.3. Residuals for detecting cell-specific lack of fit
10.4. Modeling ordinal associations
10.5. Generalized loglinear and association models, correlation models, and correspondence analysis
10.6. Empty cells and sparseness in modeling contingency tables
10.7. Bayesian loglinear modeling
11. Models for matched pairs
11.1. Comparing dependent proportions
11.2. Conditional logistic regression for binary matched pairs
11.3. Marginal models for square contingency tables
11.4. Symmetry, quasi-symmetry, and quasi-independence
11.5. Measuring agreement between observers
11.6. Bradley-terry model for paired preferences
11.7. Marginal models and quasi-symmetry models for matched sets
12. Clustered categorical data: marginal and transitional models
12.1. Marginal modeling: maximum likelihood approach
12.2. Marginal modeling: generalized estimating equations approach
12.3. Quasi-likelihood and its gee multivariate extension: details
12.4. Transitional models: markov chain and time series models
13. Clustered categorical data: random effects models
13.1. Random effects modeling of clustered categorical data
13.2. Binary responses: the logistic-normal model
13.3. Examples of random effects models for binary data
13.4. Random effects models for multinomial data
13.5. Multilevel models
13.6. GLMM fitting, inference, and prediction
13.7. Bayesian multivariate categorical modeling
14. Other mixture models for discrete data
14.1. Latent class models
14.2. Nonparametric random effects models
14.3. Beta-binomial models
14.4. Negative binomial regression
14.5. Poisson regression with random effects
15. Non-model-based classification and clustering
15.1. Classification: linear discriminant analysis
15.2. Classification: tree-structured prediction
15.3. Cluster analysis for categorical data
16. Large- and small-sample theory for parametric models
16.1. Delta method
16.2. Asymptotic distributions of estimators of model parameters and cell probabilities
16.3. Asymptotic distributions of residuals and goodness-of-fit statistics
16.4. Asymptotic distributions for logit/loglinear models
16.5. Small-sample significance tests for contingency tables
16.6. Small-sample confidence intervals for categorical data
16.7. Alternative estimation theory for parametric models
17. Historical tour of categorical data analysis
17.1. Pearson-Yule association controversy
17.2. R. A. Fisher's contributions
17.3. Logistic regression
17.4. Multiway contingency tables and loglinear models
17.5. Bayesian methods for categorical data
17.6. A look forward, and backward
Appendix A. Statistical software for categorical data analysis
Appendix B. Chi-squared distribution values.

Categorical data analysis

Ejemplares similares