THERMAL SCIENCE

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Ying Wang

,

Zai-Zai Yan

STATISTICAL INFERENCE ON THE ACCELERATED COMPETING FAILURE MODEL FROM THE INVERSE WEIBULL DISTRIBUTION UNDER PROGRESSIVELY TYPE-II CENSORED DATA

ABSTRACT
In this paper, the parameter estimation is discussed by using the maximum likelihood method when the available data have the form of progressively censored sample from a constant-stress accelerated competing failure model. Normal approximation and bootstrap confidence intervals for the unknown parameters are obtained and compared numerically. The simulation results show that bootstrap confidence intervals perform better than normal approximation. A thermal stress example is discussed.
KEYWORDS
inverse Weibull distribution, constant-stress accelerated competing failure model, Markov chain Monte Carlo method, thermal stress
PAPER SUBMITTED: 2019-12-26
PAPER REVISED: 2020-05-10
PAPER ACCEPTED: 2020-05-10
PUBLISHED ONLINE: 2021-03-27
DOI REFERENCE: https://doi.org/10.2298/TSCI191226097W
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2021, VOLUME 25, ISSUE Issue 3, PAGES [2127 - 2134]
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Statistical inference on the accelerated competing failure model from the inverse weibull distribution under progressively type-II censored data

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