[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5351/KJAS.2002.15.1.139

Bayesian Mode1 Selection and Diagnostics for Nonlinear Regression Model

나종화 (충북대학교 통계학과)
김정숙 (충북대학교 통계학과 박사과정수료)

Publication Information

The Korean Journal of Applied Statistics / v.15, no.1, 2002 , pp. 139-151 More about this Journal

Abstract

This study is concerned with model selection and diagnostics for nonlinear regression model through Bayes factor. In this paper, we use informative prior and simulate observations from the posterior distribution via Markov chain Monte Carlo. We propose the Laplace approximation method and apply the Laplace-Metropolis estimator to solve the computational difficulty of Bayes factor.

Keywords

MCMC; Metropolis-Hasting algorithm; Laplace-Metropolis; estimator; Bayes factor; Convergence diagnostics.;

Citations & Related Records

Times Cited By KSCI : 1 (Citation Analysis)

Reference
Cited By KSCI

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1	Bayesian Model Selection for Nonlinear Regression under Noninformative Prior / [Na, Jonghwa;Kim, Jeongsuk;] / Communications for Statistical Applications and Methods
2	A Comparative Study on the Performance of Bayesian Partially Linear Models / [Woo, Yoonsung;Choi, Taeryon;Kim, Wooseok;] / Communications for Statistical Applications and Methods

3	Jonghwa Na. (2003) Communications for Statistical Applications and Methods Bayesian Model Selection for Nonlinear Regression under Noninformative Prior / 10 (3) , 719
6	Yoonsung Woo. (2012) Communications for Statistical Applications and Methods A Comparative Study on the Performance of Bayesian Partially Linear Models / 19 (6) , 885

KSCI

Bayesian Mode1 Selection and Diagnostics for Nonlinear Regression Model 베이지안 비선형회귀모형의 선택과 진단

Bayesian Mode1 Selection and Diagnostics for Nonlinear Regression Model