Generalized linear mixed models with informative dropouts and missing covariates

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  1. Department of Statistics
  2. Generalized linear mixed models with informative dropouts and missing covariates

Generalized linear mixed models with informative dropouts and missing covariates

TitleGeneralized linear mixed models with informative dropouts and missing covariates
Publication TypeJournal Article
Year of Publication2006
AuthorsWu, K, WU, LANG
JournalMetrika
Volume66
Pagination1–18
Date Publishedaug
ISSN0026-1335, 1435-926X
KeywordsEconomic Theory, general, Gibbs sampling, Linearization, Probability Theory and Stochastic Processes, PX-EM algorithm, Rejection sampling, Statistics, Statistics for Business/Economics/Mathematical Finance/Insurance
AbstractGeneralized linear mixed models (GLMM) are useful in many longitudinal data analyses. In the presence of informative dropouts and missing covariates, however, standard complete-data methods may not be applicable. In this article, we consider a likelihood method and an approximate method for GLMM with informative dropouts and missing covariates. The methods are implemented by Monte–Carlo EM algorithms combined with Gibbs sampler. The approximate method may lead to inconsistent estimators but is computationally more efficient than the likelihood method. The two methods are evaluated via a simulation study for longitudinal binary data, and appear to perform reasonably well. A dataset on mental distress is analyzed in details.
URLhttp://link.springer.com/article/10.1007/s00184-006-0083-6
DOI10.1007/s00184-006-0083-6