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Bayesian Estimation of Transmuted Lomax Mixture Model with an Application to Type-I Censored Windshield Data.
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- Author(s): Mehdia, Muntazir; Aslam, Muhammad; Feroze, Navid
- Source:
Pakistan Journal of Statistics & Operation Research; 2022, Vol. 18 Issue 4, p1027-1048, 22p- Subject Terms:
- Source:
- Additional Information
- Abstract: Transmuted distributions have been centered of focus for researchers recently due to their flexibility and applicability in statistics. However, the only few contributions have considered estimation for mixture of transmuted lifetime models especially under Bayesian methods has been explored more recently. We have considered the Bayesian estimation of transmuted Lomax mixture model (TLMM) for type-I censored samples. The Bayes estimates (BEs) and posterior risks (PRs) for informative and noninformative priors are evaluated using four different loss functions (LFs), two symmetric and two asymmetric, namely the squared error loss function (SELF), precautionary loss function (PLF), weighted balance loss function (WBLF), and general entropy loss function (GELF). Simulations are run using Lindley Approximation method to compare the BEs under various sample sizes and censoring rates. The estimates under informative prior and GELF were found superior to their counterparts. The applicability of the proposed estimates has been illustrated using the analysis of a real data regarding type-I censored failure times of windshields airplanes. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Pakistan Journal of Statistics & Operation Research is the property of Pakistan Journal of Statistics & Operations Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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