Bayesian Minimum Message length87 with parametric heteroscedasticity model
| dc.contributor.author | Oloyede, Isiaka | |
| dc.date.accessioned | 2021-05-06T08:10:50Z | |
| dc.date.available | 2021-05-06T08:10:50Z | |
| dc.date.issued | 2015 | |
| dc.description.abstract | A Metropolis Hasting algorithm was adopted to perform simulation on marginal posterior distribution of heteroscedastic linear model using minimum message length87 which was conjugated with normal and inverted gamma priors to derive joint posterior distributions. The asymptotic behaviour was compared using absolute bias and mean square error criteria in order to ascertain consistency and efficiency of the estimator. The estimator is both asymptotically consistent and efficient. | en_US |
| dc.description.sponsorship | self | en_US |
| dc.identifier.uri | https://uilspace.unilorin.edu.ng/handle/20.500.12484/4974 | |
| dc.language.iso | en | en_US |
| dc.publisher | Nigerian association of Mathematical Physics | en_US |
| dc.relation.ispartofseries | ;31 | |
| dc.subject | cubic model | en_US |
| dc.subject | bootstrap | en_US |
| dc.subject | mixture experiment | en_US |
| dc.title | Bayesian Minimum Message length87 with parametric heteroscedasticity model | en_US |
| dc.type | Article | en_US |
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