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- Title
Inference for Kumaraswamy Distribution under Generalized Progressive Hybrid Censoring.
- Authors
Wang, Liang; Zhou, Ying; Lio, Yuhlong; Tripathi, Yogesh Mani
- Abstract
In this paper, generalized progressive hybrid censoring is discussed, while a scheme is designed to provide a flexible and symmetrical scenario to collect failure information in the whole life cycle of units. When the lifetime of units follows Kumaraswamy distribution, inference is investigated under classical and Bayesian approaches. The maximum likelihood estimates and associated existence and uniqueness properties are established and the confidence intervals for unknown parameters are provided by using a large sample size based on asymptotic theory. Moreover, the Bayes estimates along with highest probability density credible intervals are also developed through the Monte-Carlo Markov Chain sampling technique to approximate the associated posteriors. Simulation studies and a real-life example are presented for illustration purposes.
- Subjects
CENSORSHIP; MARKOV processes; BAYES' estimation; MONTE Carlo method; CENSORING (Statistics); SAMPLING (Process); CONFIDENCE intervals
- Publication
Symmetry (20738994), 2022, Vol 14, Issue 2, pN.PAG
- ISSN
2073-8994
- Publication type
Article
- DOI
10.3390/sym14020403