In medical and social science research, MGLMMs help disentangle state dependence from incidental parameters. Focusing on these sophisticated data analysis techniques, this work presents robust and methodologically sound models for analyzing large and complex data sets-enabling readers to answer increasingly complex research questions. It applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R. Many examples throughout the text illustrate the analysis of real-world data sets. Supporting materials are available on a Sabre-dedicated website.…mehr
In medical and social science research, MGLMMs help disentangle state dependence from incidental parameters. Focusing on these sophisticated data analysis techniques, this work presents robust and methodologically sound models for analyzing large and complex data sets-enabling readers to answer increasingly complex research questions. It applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R. Many examples throughout the text illustrate the analysis of real-world data sets. Supporting materials are available on a Sabre-dedicated website.
Damon M. Berridge is a senior lecturer in the Department of Mathematics and Statistics at Lancaster University. Dr. Berridge has nearly 20 years of experience as a statistical consultant. His research focuses on the modeling of binary and ordinal recurrent events through random effects models, with application in medical and social statistics. Robert Crouchley is a professor of applied statistics and director of the Centre for e-Science at Lancaster University. His research interests involve the development of statistical methods and software for causal inference in nonexperimental data. These methods include models for errors in variables, missing data, heterogeneity, state dependence, nonstationarity, event history data, and selection effects.
Inhaltsangabe
Introduction. Generalized Linear Models for Continuous/Interval Scale Data. Generalized Linear Models for Other Types of Data. Family of Generalized Linear Models. Mixed Models for Continuous/Interval Scale Data. Mixed Models for Binary Data. Mixed Models for Ordinal Data. Mixed Models for Count Data. Family of Two-Level Generalized Linear Models. Three-Level Generalized Linear Models. Models for Multivariate Data. Models for Duration and Event History Data. Stayers, Non-Susceptibles, and Endpoints. Handling Initial Conditions/State Dependence in Binary Data. Incidental Parameters: An Empirical Comparison of Fixed Effects and Random Effects Models. Appendices. Bibliography.
Introduction. Generalized Linear Models for Continuous/Interval Scale Data. Generalized Linear Models for Other Types of Data. Family of Generalized Linear Models. Mixed Models for Continuous/Interval Scale Data. Mixed Models for Binary Data. Mixed Models for Ordinal Data. Mixed Models for Count Data. Family of Two-Level Generalized Linear Models. Three-Level Generalized Linear Models. Models for Multivariate Data. Models for Duration and Event History Data. Stayers, Non-Susceptibles, and Endpoints. Handling Initial Conditions/State Dependence in Binary Data. Incidental Parameters: An Empirical Comparison of Fixed Effects and Random Effects Models. Appendices. Bibliography.
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