Nonlinear mixed-effect models with nonignorably missing covariates

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Nonlinear mixed-effect models with nonignorably missing covariates

TitleNonlinear mixed-effect models with nonignorably missing covariates
Publication TypeJournal Article
Year of Publication2004
AuthorsWU, LANG
JournalCanadian Journal of Statistics
Volume32
Pagination27–37
Date Publishedmar
ISSN1708-945X
KeywordsEM algorithm, Gibbs sampling, Longitudinal data, missing data, Rejection sampling
AbstractNonlinear mixed-effect models are often used in the analysis of longitudinal data. However, it sometimes happens that missing values for some of the model covariates are not purely random. Motivated by an application to HTV viral dynamics, where this situation occurs, the author considers likelihood inference for this type of problem. His approach involves a Monte Carlo EM algorithm, along with a Gibbs sampler and rejection/importance sampling methods. A concrete application is provided.
URLhttp://onlinelibrary.wiley.com/doi/10.2307/3315997/abstract
DOI10.2307/3315997