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Prediction of Kumaraswamy distribution in constant‐stress model based on type‐I hybrid censored data
Author(s) -
Fawzy Mohamad A.
Publication year - 2020
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.11452
Subject(s) - markov chain monte carlo , bayesian probability , mathematics , statistics , computer science , algorithm
Abstract In this work, a particular problem of Bayesian prediction concerning future observation from Kumaraswamy distribution under constant‐stress partially accelerated life test is treated. Type‐I hybrid censored data of the observed data are utilized. One‐ and two‐sample Bayesian prediction intervals for an unobserved future sample from Kumaraswamy distribution are settled. Markov chain Monte Carlo (MCMC) procedure is used to get Bayesian predictive intervals. Lastly, simulation study and a numerical example are given to illustrate the consequences of the research.