Adaptive Type-II Hybrid Progressive Censoring Samples for Statistical Inference of Comparative Inverse Weibull Distributions

被引:3
|
作者
Al-Essa, Laila A. [1 ]
Soliman, Ahmed A. [2 ]
Abd-Elmougod, Gamal A. [3 ]
Alshanbari, Huda M. [1 ]
机构
[1] Princess Nourah Bint Abdulrahman Univ, Coll Sci, Dept Math Sci, POB 84428, Riyadh 11671, Saudi Arabia
[2] Sohag Univ, Fac Sci, Dept Math, Sohag 82524, Egypt
[3] Damanhour Univ, Fac Sci, Dept Math, Damanhour 2251, Egypt
关键词
inverse Weibull distribution; comparative life tests; adaptive hybrid censoring scheme; Bayes estimation; maximum likelihood estimation; bootstrap techniques; EXACT LIKELIHOOD INFERENCE; 2 EXPONENTIAL POPULATIONS; METHODOLOGY;
D O I
10.3390/axioms12100973
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Recently, there has been a lot of interest in comparative life testing for items under jointly censored schemes for products from multiple production lines. The inverse Weibull distribution (IWD) is commonly used in life testing and reliability theory. In this paper, we address the problem of statistical inference from comparative inverse Weibull distributions under joint samples. An adaptive type-II hybrid progressive censoring scheme (HPCS) is used to save the balance between the ideal test time and the number of observed failures. Under the adaptive type-II HPCS, unknown parameters of the inverse Weibull populations are estimated using both maximum likelihood and Bayesian approaches. Asymptotic confidence intervals are established using the observed Fisher information matrix and bootstrap confidence intervals. We suggest using Markov chain Monte Carlo (MCMC) techniques to compute credible intervals under independent gamma priors. Using Monte Carlo simulations, all theoretical conclusions are tested and contrasted. For illustration purposes, an actual sample from comparative populations is analysed.
引用
收藏
页数:17
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