Open Access Open Access  Restricted Access Subscription or Fee Access

Bayesian Using Extension Jeffreys Prior for Weibull Regression Censored Data

Al Omari Mohammed Ahmed

Abstract



With regards to the Bayesian, we have developed an approach using Jeffreys prior and extension Jeffreys prior with covariate obtained by using Gauss quadrature method. This is also done for maximum likelihood estimator to estimate the parameters of the covariate of the
Weibull regression distribution given shape with Type I censored data. It is seen that the estimators obtained are not available in closed forms although they can solve it using suitable numerical methods. The comparison criteria are the mean square errors. The performance of
these three estimates is assessed using simulation, considering various sample sizes, several specific values of Weibull shape parameter. The results show that extension Jeffreys prior is a better estimator than others.

Keywords


extension Jeffreys prior, Weibull distribution, Bayesian method, Type I censoring, Gauss quardature.

Full Text:

PDF


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.