Inference for Kumaraswamy Distribution under Generalized Progressive Hybrid Censoring

Symmetry

Yuhlong Lio¹  Yogesh Mani Tripathi²  Liang Wang³  Ying Zhou³ 

[1] Department of Mathematical Sciences, University of South Dakota, Vermillion, SD 57069, USA;Department of Mathematics, Indian Institute of Technology Patna, Patna 800013, India;School of Mathematics, Yunnan Normal University, Kunming 650500, China;

关键词: Kumaraswamy distribution;    generalized progressive hybrid censoring;    maximum likelihood estimation;    approximation theory;    Monte-Carlo simulation;   

DOI  :  10.3390/sym14020403

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown