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Bayesian inference for linear regression under alpha-skew-normal prior
Alhamide, A.A1, Kamarulzaman Ibrahim2, Alodat, M.T3, Wan Zawiah Wan Zin4.
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.
Affiliation:
- Qatar University, Qatar
- Universiti Kebangsaan Malaysia, Malaysia
- Qatar University, Qatar
- Universiti Kebangsaan Malaysia, Malaysia
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Indexed by |
MyJurnal (2021) |
H-Index
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6 |
Immediacy Index
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0.000 |
Rank |
0 |
Indexed by |
Web of Science (SCIE - Science Citation Index Expanded) |
Impact Factor
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JCR (1.009) |
Rank |
Q4 (Multidisciplinary Sciences) |
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JCI (0.15) |
Indexed by |
Scopus 2020 |
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CiteScore (1.4) |
Rank |
Q2 (Multidisciplinary) |
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SJR (0.251) |
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