Gibbs sampling in DP-based nonlinear mixed effects models

被引：0

作者：

Wang, Jing ^{[1
]}

机构：

[1] Louisiana State Univ, Dept Expt Stat, Baton Rouge, LA 70803 USA

来源：

JOURNAL OF APPLIED STATISTICS | 2010年 / 37卷 / 02期

关键词：

nonlinear mixed effects model; Dirichlet process; Laplace's approximation; adaptive Gaussian quadrature approximation; No-gaps algorithm; EM algorithm; Monte Carlo approximations; Markov chain; DIRICHLET PROCESS MIXTURE; NONPARAMETRIC PROBLEMS; APPROXIMATIONS; INFERENCE;

D O I：

10.1080/02664760903117721

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This article uses several approaches to deal with the difficulty involved in evaluating the intractable integral when using Gibbs sampling to estimate the nonlinear mixed effects model (NLMM) based on the Dirichlet process (DP). For illustration, we applied these approaches to real data and simulations. Comparisons are then made between these methods with respect to estimation accuracy and computing efficiency.

引用

页码：325 / 340

页数：16

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