Statistical inference for dependent competing risks data under adaptive Type-II progressive hybrid censoring
© 2025 Informa UK Limited, trading as Taylor & Francis Group.
| Veröffentlicht in: | Journal of applied statistics. - 1991. - 52(2025), 10 vom: 06., Seite 1871-1903 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2025
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| Zugriff auf das übergeordnete Werk: | Journal of applied statistics |
| Schlagworte: | Journal Article 62E15 62F10 62F15 Adaptive type II progressive hybrid censoring MCMC convergence dependent competing risk gamma-dirichlet prior optimality |
| Zusammenfassung: | © 2025 Informa UK Limited, trading as Taylor & Francis Group. In this article, we consider statistical inference based on dependent competing risks data from Marshall-Olkin bivariate Weibull distribution. The maximum likelihood estimates of the unknown model parameters have been computed by using Newton-Raphson method under adaptive Type II progressive hybrid censoring with partially observed failure causes. Existence and uniqueness of maximum likelihood estimates are derived. Approximate confidence intervals have been constructed via the observed Fisher information matrix using asymptotic normality property of the maximum likelihood estimates. Bayes estimates and highest posterior density credible intervals have been calculated under gamma-Dirichlet prior distribution by using Markov chain Monte Carlo technique. Convergence of Markov chain Monte Carlo samples is tested. In addition, a Monte Carlo simulation is carried out to compare the effectiveness of the proposed methods. Further, three different optimality criteria have been taken into account to obtain the most effective censoring plans. From these simulation study results it has been concluded that Bayesian technique produces superior outcomes. Finally, a real-life data set has been analyzed to illustrate the operability and applicability of the proposed methods |
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| Beschreibung: | Date Revised 06.08.2025 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
| ISSN: | 0266-4763 |
| DOI: | 10.1080/02664763.2024.2445237 |