2017/06/23 by Shuvashree Mondal, Debasis Kundu, Mondal, Shuvashree +1
Decision Sciences · Engineering · Mathematics · #Applications (stat.AP) #FOS: Computer and information sciences #Methodology (stat.ME) #Probabilistic and Robust Engineering Design #Reliability and Maintenance Optimization #Statistical Distribution Estimation and Applications
paper · pdf · doi:10.48550/arxiv.1706.07682
openalex publication_date 2017/06/23 · openalex created_date 2022/08/17 · openalex updated_date 2026/07/28
The analysis of progressively censored data has received considerable\nattention in the last few years. In this paper we consider the joint\nprogressive censoring scheme for two populations. It is assumed that the\nlifetime distribution of the items from the two populations follow Weibull\ndistribution with the same shape but different scale parameters. Based on the\njoint progressive censoring scheme first we consider the maximum likelihood\nestimators of the unknown parameters whenever they exist. We provide the\nBayesian inferences of the unknown parameters under a fairly general priors on\nthe shape and scale parameters. The Bayes estimators and the associated\ncredible intervals cannot be obtained in closed form, and we propose to use the\nimportance sampling technique to compute the same. Further, we consider the\nproblem when it is known apriori that the expected lifetime of one population\nis smaller than the other. We provide the order restricted classical and\nBayesian inferences of the unknown parameters. Monte Carlo simulations are\nperformed to observe the performances of the different estimators and the\nassociated confidence and credible intervals. One real data set has been\nanalyzed for illustrative purpose.\n