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Optimal sampling and statistical inferences for Kumaraswamy distribution under progressive Type-II censoring schemes

التفاصيل البيبلوغرافية
العنوان: Optimal sampling and statistical inferences for Kumaraswamy distribution under progressive Type-II censoring schemes
المؤلفون: Osama E. Abo-Kasem, Ahmed R. El Saeed, Amira I. El Sayed
المصدر: Scientific Reports, Vol 13, Iss 1, Pp 1-30 (2023)
بيانات النشر: Nature Portfolio, 2023.
سنة النشر: 2023
المجموعة: LCC:Medicine
LCC:Science
مصطلحات موضوعية: Medicine, Science
الوصف: Abstract In this paper, we study non-Bayesian and Bayesian estimation of parameters for the Kumaraswamy distribution based on progressive Type-II censoring. First, the maximum likelihood estimates and maximum product spacings are derived. In addition, we derive the asymptotic distribution of the parameters and the asymptotic confidence intervals. Second, Bayesian estimators under symmetric and asymmetric loss functions (Squared error, linear exponential, and general entropy loss functions) are also obtained. The Lindley approximation and the Markov chain Monte Carlo method are used to derive the Bayesian estimates. Furthermore, we derive the highest posterior density credible intervals of the parameters. We further present an optimal progressive censoring scheme among different competing censoring scheme using three optimality criteria. Simulation studies are conducted to evaluate the performance of the point and interval estimators. Finally, one application of real data sets is provided to illustrate the proposed procedures.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2045-2322
العلاقة: https://doaj.org/toc/2045-2322Test
DOI: 10.1038/s41598-023-38594-9
الوصول الحر: https://doaj.org/article/635eabb2992f480798d637cc5501c7cfTest
رقم الانضمام: edsdoj.635eabb2992f480798d637cc5501c7cf
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20452322
DOI:10.1038/s41598-023-38594-9