A computationally efficient method for nonlinear mixed-effects models with nonignorable missing data in time-varying covariates

DOI10.1016/J.CSDA.2006.07.036zbMath1161.62381OpenAlexW2012273085MaRDI QIDQ1019870

Lang Wu

Publication date: 29 May 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2006.07.036

zbMATH Keywords

EM algorithmlongitudinal datalinearizationrandom effects model

Mathematics Subject Classification ID

Point estimation (62F10) General nonlinear regression (62J02)

Related Items (3)

Approximate bounded influence estimation for longitudinal data with outliers and measurement errors ⋮ Fully PCA-based approach to optimization of multiresponse-multistage problems with stochastic considerations ⋮ Some asymptotic results for semiparametric nonlinear mixed-effects models with incomplete data

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• Exact and Approximate Inferences for Nonlinear Mixed-Effects Models With Missing Covariates

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