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AuthorAlhamide, A.A.
AuthorKAMARULZAMAN, Ibrahim
AuthorAlodat, M.T.
AuthorZin, WAN ZAWIAH WAN
Available date2020-06-23T20:45:40Z
Publication Date2019
Publication NameSains Malaysiana
ResourceScopus
ISSN1266039
URIhttp://dx.doi.org/10.17576/jsm-2019-4801-26
URIhttp://hdl.handle.net/10576/15102
AbstractA 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. - 2019 Penerbit Universiti Kebangsaan Malaysia. All rights reserved.
Languageen
PublisherPenerbit Universiti Kebangsaan Malaysia
SubjectABSTRAK
Alpha skew normal distribution
Bayesian linear regression model
Simulation
TitleBayesian inference for linear regression under alpha-skew-normal prior [Pentaabiran Bayesian untuk Model Regresi Linear Prior Normal-Pencong-Alfa]
TypeArticle
Pagination227-235
Issue Number1
Volume Number48
dc.accessType Open Access


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