Gibbs sampling in DP-based nonlinear mixed effects models

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.

MoreLess

Year of publication:	2010
Authors:	Wang, Jing
Published in:	Journal of Applied Statistics. - Taylor & Francis Journals, ISSN 0266-4763. - Vol. 37.2010, 2, p. 325-340
Publisher:	Taylor & Francis Journals
Subject:	nonlinear mixed effects model \| Dirichlet process \| Laplace's approximation \| adaptive Gaussian quadrature approximation \| No-gaps algorithm \| EM algorithm \| Monte Carlo approximations \| Markov chain

Online Resource

Check full text access |

More access options

Check Google Scholar

In libraries world-wide (WorldCat)

In German libraries (KVK)

subito order

I need help

More details

Extent:	text/html
Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10008582911