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Parameters estimation of Burr‐XII distribution under first‐failure progressively unified hybrid censoring schemes
Author(s) -
Jia Junmei,
Yan Zaizai,
Peng Xiuyun
Publication year - 2018
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11391
Subject(s) - censoring (clinical trials) , confidence interval , estimator , bayes' theorem , statistics , mathematics , exponential distribution , monte carlo method , point estimation , algorithm , computer science , bayesian probability
In this paper, a new life test plan called a first‐failure progressively unified hybrid censoring scheme is introduced. Based on this type of censoring scheme, we obtain the maximum likelihood and the Bayes estimators of the unknown parameters from the Burr‐XII distribution. The asymptotic confidence intervals, two kinds of bootstrap confidence intervals and highest posterior density credible intervals of the unknown parameters are constructed. Analysis of a real data set is present to demonstrate the application of the proposed method. The performances of the point and interval estimations are compared by using a Monte Carlo simulation study.