Log-linear Models for Event Histories (Advanced Quantitative Techniques in the Social Sciences Series v. 8)
By: Jeroen K. Vermunt (author)Hardback
1 - 2 weeks availability
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, modified path models with latent variables and log-linear models for non-response.Other topics covered are: the main types of hazard models; censoring; the use of time-varying covariates; models for competing risks; multivariate hazard models; and a general approach for dealing with missing data problems - including measurement error in the dependent variable, measurement error in the covariates, partially missing information in the dependent variable and partially observed covariate values.
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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- ID: 9780761909378
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