Bayesian inference for linear regression under alpha-skew-normal prior

Alhamide, A.A. and Kamarulzaman Ibrahim, and Alodat, M.T. and Wan Zawiah Wan Zin, (2019) Bayesian inference for linear regression under alpha-skew-normal prior. Sains Malaysiana, 48 (1). pp. 227-235. ISSN 0126-6039


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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.

Item Type:Article
Keywords:Alpha skew normal distribution; Bayesian linear regression model; Simulation
Journal:Sains Malaysiana
ID Code:13072
Deposited By: ms aida -
Deposited On:14 Jun 2019 03:14
Last Modified:20 Jun 2019 14:55

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