Pentaabiran Bayesian untuk Model Regresi Linear Prior Normal-Pencong-Alfa

Translated title of the contribution: Bayesian inference for linear regression under alpha-skew-normal prior

Research output: Contribution to journalArticle

Abstract

A study on Bayesian inference for the linear regression model is carried out in the case when the prior distribution for the regression parameters is assumed to follow the alpha-skew-normal distribution. The posterior distribution and its associated full conditional distributions are derived. Then, the Bayesian point estimates and credible intervals for the regression parameters are determined based on a simulation study using the Markov chain Monte Carlo method. The parameter estimates and intervals obtained are compared with their counterparts when the prior distributions are assumed either normal or non-informative. In addition, the findings are applied to Scottish hills races data. It appears that when the data are skewed, the alpha-skew-normal prior contributes to a more precise estimate of the regression parameters as opposed to the other two priors.

Original languageMalay
Pages (from-to)227-235
Number of pages9
JournalSains Malaysiana
Volume48
Issue number1
DOIs
Publication statusPublished - 1 Jan 2019

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Bayesian inference
Linear regression
Skew
Regression
Prior distribution
Credible Interval
Skew-normal Distribution
Point Estimate
Markov Chain Monte Carlo Methods
Conditional Distribution
Linear Regression Model
Posterior distribution
Estimate
Simulation Study
Interval

ASJC Scopus subject areas

  • General

Cite this

Pentaabiran Bayesian untuk Model Regresi Linear Prior Normal-Pencong-Alfa. / Alhamide, A. A.; Ibrahim, Kamarulzaman; Alodat, M. T.; Wan Zin @ Wan Ibrahim, Wan Zawiah.

In: Sains Malaysiana, Vol. 48, No. 1, 01.01.2019, p. 227-235.

Research output: Contribution to journalArticle

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