Some asymptotic results for semiparametric nonlinear mixed-effects models with incomplete data

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Some asymptotic results for semiparametric nonlinear mixed-effects models with incomplete data

TitleSome asymptotic results for semiparametric nonlinear mixed-effects models with incomplete data
Publication TypeJournal Article
Year of Publication2010
AuthorsLiu, W, WU, LANG
JournalJournal of Statistical Planning and Inference
Volume140
Pagination52–64
Date Publishedjan
ISSN0378-3758
KeywordsApproximation, Asymptotics, Longitudinal data, Measurement error
AbstractIn modeling complex longitudinal data, semiparametric nonlinear mixed-effects (SNLME) models are very flexible and useful. Covariates are often introduced in the models to partially explain the inter-individual variations. In practice, data are often incomplete in the sense that there are often measurement errors and missing data in longitudinal studies. The likelihood method is a standard approach for inference for these models but it can be computationally very challenging, so computationally efficient approximate methods are quite valuable. However, the performance of these approximate methods is often based on limited simulation studies, and theoretical results are unavailable for many approximate methods. In this article, we consider a computationally efficient approximate method for a class of SNLME models with incomplete data and investigate its theoretical properties. We show that the estimates based on the approximate method are consistent and asymptotically normally distributed.
URLhttp://www.sciencedirect.com/science/article/pii/S0378375809001888
DOI10.1016/j.jspi.2009.06.006