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An approximate method for nonlinear mixed-effects models with nonignorably missing covariates

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  • Wu, Lang

Abstract

Nonlinear mixed-effect (NLME) models are very useful in many longitudinal studies. In practice, covariates in NLME models may contain missing data, and the missing data may be nonignorable. Likelihood inference for NLME models with missing covariates can be computationally very intensive. We propose a computationally much more efficient approximate method for NLME models with nonignorably missing covariates. We illustrate the method using a real data example.

Suggested Citation

  • Wu, Lang, 2008. "An approximate method for nonlinear mixed-effects models with nonignorably missing covariates," Statistics & Probability Letters, Elsevier, vol. 78(4), pages 384-389, March.
    • Handle: RePEc:eee:stapro:v:78:y:2008:i:4:p:384-389
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    References listed on IDEAS

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    1. Lang Wu, 2004. "Exact and Approximate Inferences for Nonlinear Mixed-Effects Models With Missing Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 700-709, January.
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