Preference of Prior for Two-Component Mixture of Lomax Distribution
Recently, El-Sherpieny et al., (2020), suggested Type-II hybrid censoring method for parametric estimation of Lomax distribution (LD) without due regard being given to the choice of priors and posterior risk associated with the model. This paper fills this gap and derived the new LD model with minim...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Springer
2021-06-01
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| Series: | Journal of Statistical Theory and Applications (JSTA) |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/125958275/view |
| _version_ | 1828346688555712512 |
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| author | Faryal Younis Muhammad Aslam M. Ishaq Bhatti |
| author_facet | Faryal Younis Muhammad Aslam M. Ishaq Bhatti |
| author_sort | Faryal Younis |
| collection | DOAJ |
| description | Recently, El-Sherpieny et al., (2020), suggested Type-II hybrid censoring method for parametric estimation of Lomax distribution (LD) without due regard being given to the choice of priors and posterior risk associated with the model. This paper fills this gap and derived the new LD model with minimum posterior risk for the selection of priors. It derives a closed form expression for Bayes estimates and posterior risks using square error loss function (SELF), weighted loss function (WLF), quadratic loss function (QLF) and DeGroot loss function (DLF). Prior predictive approach is used to elicit the hyperparameters of mixture model. Analysis of Bayes estimates and posterior risks is presented in terms of sample size n, mixing proportion p and censoring rate t0, with the help of simulation study. Usefulness of the model is demonstrated on applying it to simulated and real-life data which show promising results in terms of better estimation and risk reduction. |
| first_indexed | 2024-04-14T00:30:25Z |
| format | Article |
| id | doaj.art-894175252c0f47cf8210b1d70fe33598 |
| institution | Directory Open Access Journal |
| issn | 2214-1766 |
| language | English |
| last_indexed | 2024-04-14T00:30:25Z |
| publishDate | 2021-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of Statistical Theory and Applications (JSTA) |
| spelling | doaj.art-894175252c0f47cf8210b1d70fe335982022-12-22T02:22:33ZengSpringerJournal of Statistical Theory and Applications (JSTA)2214-17662021-06-0120210.2991/jsta.d.210616.002Preference of Prior for Two-Component Mixture of Lomax DistributionFaryal YounisMuhammad AslamM. Ishaq BhattiRecently, El-Sherpieny et al., (2020), suggested Type-II hybrid censoring method for parametric estimation of Lomax distribution (LD) without due regard being given to the choice of priors and posterior risk associated with the model. This paper fills this gap and derived the new LD model with minimum posterior risk for the selection of priors. It derives a closed form expression for Bayes estimates and posterior risks using square error loss function (SELF), weighted loss function (WLF), quadratic loss function (QLF) and DeGroot loss function (DLF). Prior predictive approach is used to elicit the hyperparameters of mixture model. Analysis of Bayes estimates and posterior risks is presented in terms of sample size n, mixing proportion p and censoring rate t0, with the help of simulation study. Usefulness of the model is demonstrated on applying it to simulated and real-life data which show promising results in terms of better estimation and risk reduction.https://www.atlantis-press.com/article/125958275/viewMixture of Lomax distributionCensored samplingElicitation of hyperparameterBayes estimatorPosterior riskLoss function |
| spellingShingle | Faryal Younis Muhammad Aslam M. Ishaq Bhatti Preference of Prior for Two-Component Mixture of Lomax Distribution Journal of Statistical Theory and Applications (JSTA) Mixture of Lomax distribution Censored sampling Elicitation of hyperparameter Bayes estimator Posterior risk Loss function |
| title | Preference of Prior for Two-Component Mixture of Lomax Distribution |
| title_full | Preference of Prior for Two-Component Mixture of Lomax Distribution |
| title_fullStr | Preference of Prior for Two-Component Mixture of Lomax Distribution |
| title_full_unstemmed | Preference of Prior for Two-Component Mixture of Lomax Distribution |
| title_short | Preference of Prior for Two-Component Mixture of Lomax Distribution |
| title_sort | preference of prior for two component mixture of lomax distribution |
| topic | Mixture of Lomax distribution Censored sampling Elicitation of hyperparameter Bayes estimator Posterior risk Loss function |
| url | https://www.atlantis-press.com/article/125958275/view |
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