Event history analysis has been a useful method in the social sciences for studying the processes of social change. However, a main difficulty in using this technique is to observe all relevant explanatory variables without missing any variables. This book presents a general approach to missing data problems in event history analysis which is based on the similarities between log-linear models, hazard models and event history models. It begins with a discussion of log-rate models, modified path models and methods for obtaining maximum likelihood estimates of the parameters of log-linear…mehr
Event history analysis has been a useful method in the social sciences for studying the processes of social change. However, a main difficulty in using this technique is to observe all relevant explanatory variables without missing any variables. This book presents a general approach to missing data problems in event history analysis which is based on the similarities between log-linear models, hazard models and event history models. It begins with a discussion of log-rate models, modified path models and methods for obtaining maximum likelihood estimates of the parameters of log-linear models. The author then shows how to incorporate variables with missing information in log-linear models - including latent class models, mHinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Jeroen K. Vermunt is a full professor in the Department of Methodology and Statistics at Tilburg University, the Netherlands. His research is on methodology of social, behavioral, and biomedical research, with a special focus on latent variable models and techniques for the analysis of categorical, multilevel, and longitudinal data sets. He has widely published on these topics in statistical and methodological journals and has also coauthored many articles in applied journals in which these methods are used to solve practical research problems. He is the codeveloper (with Jay Magidson) of the Latent GOLD software package. In 2005, Vermunt was awarded the Leo Goodman award by the Methodology Section of the American Sociological Association. His full CV and publications can be found at www.jeroenvermunt.nl.
Inhaltsangabe
Introduction Log-Linear Anaylsis Log-Linear Anaylsis with Latent Variables and Missing Data Event History Analysis Event History Analysis with Latent Variables and Missing Data A: Computation of the Log-Linear Parameters When Using the IPF Algorithm B: The Log-Linear Model as One of the Generalized Linear Models C: The Newton-Raphson Algorithm D: The Uni-Dimensional Newton Algorithm E: Likelihood Equations for Modified Path Models F: The Estimation of Conditional Probabilities under Restrictions G: The Information Matrix in Modified Path Models with Missing Data
Introduction Log-Linear Anaylsis Log-Linear Anaylsis with Latent Variables and Missing Data Event History Analysis Event History Analysis with Latent Variables and Missing Data A: Computation of the Log-Linear Parameters When Using the IPF Algorithm B: The Log-Linear Model as One of the Generalized Linear Models C: The Newton-Raphson Algorithm D: The Uni-Dimensional Newton Algorithm E: Likelihood Equations for Modified Path Models F: The Estimation of Conditional Probabilities under Restrictions G: The Information Matrix in Modified Path Models with Missing Data
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