THE NONLINEAR MIXED EFFECTS MODEL WITH A SMOOTH RANDOM EFFECTS DENSITY

• 成果类型: Article
• 署名作者: DAVIDIAN, M; GALLANT, AR
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/80.3.475
• 发表日期: 1993
• 页码: 475488
• 关键词: maximum-likelihood estimation; Asymptotic Normality; population pharmacokinetics; regression-models

摘要

The fixed parameters of the nonlinear mixed effects model and the density of the random effects are estimated jointly by maximum likelihood. The density of the random effects is assumed to be smooth but is otherwise unrestricted. The method uses a series expansion that follows from the smoothness assumption to represent the density and quadrature to compute the likelihood. Standard algorithms are used for optimization. Empirical Bayes estimates of random coefficients are obtained by computing posterior modes. The method is applied to data. from pharmacokinetics, and properties of the method are investigated by application to simulated data.