Gibbs sampling in DP-based nonlinear mixed effects models

Jing Wang

doi:10.1080/02664760903117721

Gibbs sampling in DP-based nonlinear mixed effects models

Jing Wang

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	325-340
Number of pages	16
Journal	Journal of Applied Statistics
Volume	37
Issue number	2
DOIs	https://doi.org/10.1080/02664760903117721
State	Published - Feb 2010

Keywords

Adaptive Gaussian quadrature approximation
Dirichlet process
EM algorithm
Laplace's approximation
Markov chain
Monte Carlo approximations
No-gaps algorithm
Nonlinear mixed effects model

Access to Document

10.1080/02664760903117721

Cite this

@article{b6caca6f684148a792400ec36756c3f0,

title = "Gibbs sampling in DP-based nonlinear mixed effects models",

abstract = "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.",

keywords = "Adaptive Gaussian quadrature approximation, Dirichlet process, EM algorithm, Laplace's approximation, Markov chain, Monte Carlo approximations, No-gaps algorithm, Nonlinear mixed effects model",

author = "Jing Wang",

year = "2010",

month = feb,

doi = "10.1080/02664760903117721",

language = "English",

volume = "37",

pages = "325--340",

journal = "Journal of Applied Statistics",

issn = "0266-4763",

number = "2",

}

TY - JOUR

T1 - Gibbs sampling in DP-based nonlinear mixed effects models

AU - Wang, Jing

PY - 2010/2

Y1 - 2010/2

N2 - 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.

AB - 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.

KW - Adaptive Gaussian quadrature approximation

KW - Dirichlet process

KW - EM algorithm

KW - Laplace's approximation

KW - Markov chain

KW - Monte Carlo approximations

KW - No-gaps algorithm

KW - Nonlinear mixed effects model

UR - https://www.scopus.com/pages/publications/75649111180

U2 - 10.1080/02664760903117721

DO - 10.1080/02664760903117721

M3 - Article

AN - SCOPUS:75649111180

SN - 0266-4763

VL - 37

SP - 325

EP - 340

JO - Journal of Applied Statistics

JF - Journal of Applied Statistics

IS - 2

ER -

Gibbs sampling in DP-based nonlinear mixed effects models

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this