Generalized linear models for categorical and continuous limited dependent variables
- Michael Smithson, Edgar C. Merkle.
- Boca Raton : CRC Press, 
- Physical description
- xxiii, 284 pages : illustrations ; 24 cm.
Math & Statistics Library
QA279 .S634 2014
- Unknown QA279 .S634 2014
- Includes bibliographical references (pages 261-274) and indexes.
- Introduction and Overview The Nature of Limited Dependent Variables Overview of GLMs Estimation Methods and Model Evaluation Organization of This Book Discrete Variables Binary Variables Logistic Regression The Binomial GLM Estimation Methods and Issues Analyses in R and Stata Exercises Nominal Polytomous Variables Multinomial Logit Model Conditional Logit and Choice Models Multinomial Processing Tree Models Estimation Methods and Model Evaluation Analyses in R and Stata Exercises Ordinal Categorical Variables Modeling Ordinal Variables: Common Practice versus Best Practice Ordinal Model Alternatives Cumulative Models Adjacent Models Stage Models Estimation Methods and Issues Analyses in R and Stata Exercises Count Variables Distributions for Count Data Poisson Regression Models Negative Binomial Models Truncated and Censored Models Zero-Inflated and Hurdle Models Estimation Methods and Issues Analyses in R and Stata Exercises Continuous Variables Doubly Bounded Continuous Variables Doubly Bounded versus Censored The beta GLM Modeling Location and Dispersion Estimation Methods and Issues Zero- and One-Inflated Models Finite Mixture Models Analyses in R and Stata Exercises Censoring and Truncation Models for Censored and Truncated Variables Non-Gaussian Censored Regression Estimation Methods, Model Comparison, and Diagnostics Extensions of Censored Regression Models Analyses in R and Stata Exercises Extensions Extensions and Generalizations Multilevel Models Bayesian Estimation Evaluating Relative Importance of Predictors in GLMs.
- (source: Nielsen Book Data)
- Publisher's Summary
- Generalized Linear Models for Categorical and Continuous Limited Dependent Variables is designed for graduate students and researchers in the behavioral, social, health, and medical sciences. It incorporates examples of truncated counts, censored continuous variables, and doubly bounded continuous variables, such as percentages. The book provides broad, but unified, coverage, and the authors integrate the concepts and ideas shared across models and types of data, especially regarding conceptual links between discrete and continuous limited dependent variables. The authors argue that these dependent variables are, if anything, more common throughout the human sciences than the kind that suit linear regression. They cover special cases or extensions of models, estimation methods, model diagnostics, and, of course, software. They also discuss bounded continuous variables, boundary-inflated models, and methods for modeling heteroscedasticity. Wherever possible, the authors have illustrated concepts, models, and techniques with real or realistic datasets and demonstrations in R and Stata, and each chapter includes several exercises at the end. The illustrations and exercises help readers build conceptual understanding and fluency in using these techniques. At several points the authors bring together material that has been previously scattered across the literature in journal articles, software package documentation files, and blogs. These features help students learn to choose the appropriate models for their purpose.
(source: Nielsen Book Data)
- Publication date
- Statistics in the social and behavioral sciences
- 9781466551732 (hardback)
- 1466551739 (hardback)