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Inference for two‐parameter Rayleigh competing risks data under generalized progressive hybrid censoring
Author(s) -
Singh Devendra Pratap,
Lodhi Chandrakant,
Tripathi Yogesh Mani,
Wang Liang
Publication year - 2021
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2791
Subject(s) - censoring (clinical trials) , mathematics , statistics , fisher information , bayes' theorem , gibbs sampling , scale parameter , rayleigh distribution , inference , monte carlo method , independent and identically distributed random variables , importance sampling , confidence interval , point estimation , bayesian probability , probability density function , computer science , random variable , artificial intelligence
Abstract In this paper, a competing risks model based on the generalized progressive hybrid censored two‐parameter Rayleigh distributions is studied under the assumption that the lifetime distributions of failure causes are identically distributed with same location and different scale parameters. We obtain maximum likelihood estimates of unknown parameters with associated existence uniqueness. The approximate confidence intervals are constructed using the asymptotic distribution of maximum likelihood estimates via the observed information matrix. Further, Bayes point estimates and the highest probability density credible intervals of unknown parameters are presented, and the Gibbs sampling technique is used to approximate corresponding estimates. A Monte Carlo simulation study is conducted to compare the accuracy of proposed estimates. Finally, a real‐life example is presented for illustration purpose.