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Permissible noninformative priors for the accelerated life test model with censored data

In this paper, the Jeffreys priors for the step-stress partially accelerated life test with Type-II adaptive progressive hybrid censoring scheme data are considered. Given a density function family satisfied certain regularity conditions, the Fisher information matrix and Jeffreys priors are obtaine...

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Detalles Bibliográficos
Autores principales: Zhang, Fode, Shi, Yimin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4807206/
https://www.ncbi.nlm.nih.gov/pubmed/27065208
http://dx.doi.org/10.1186/s40064-016-2004-0
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author Zhang, Fode
Shi, Yimin
author_facet Zhang, Fode
Shi, Yimin
author_sort Zhang, Fode
collection PubMed
description In this paper, the Jeffreys priors for the step-stress partially accelerated life test with Type-II adaptive progressive hybrid censoring scheme data are considered. Given a density function family satisfied certain regularity conditions, the Fisher information matrix and Jeffreys priors are obtained. Taking the Weibull distribution as an example, the Jeffreys priors, posterior analysis and its permissibility are discussed. The results, which present that how the accelerated stress levels, censored size, hybrid censoring time and stress change time etc. affect the Jeffreys priors, are obtained. In addition, a theorem which shows there exists a relationship between single observation and multi observations for permissible priors is proved. Finally, using Metroplis with in Gibbs sampling algorithm, these factors are confirmed by computing the frequentist coverage probabilities.
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spelling pubmed-48072062016-04-09 Permissible noninformative priors for the accelerated life test model with censored data Zhang, Fode Shi, Yimin Springerplus Research In this paper, the Jeffreys priors for the step-stress partially accelerated life test with Type-II adaptive progressive hybrid censoring scheme data are considered. Given a density function family satisfied certain regularity conditions, the Fisher information matrix and Jeffreys priors are obtained. Taking the Weibull distribution as an example, the Jeffreys priors, posterior analysis and its permissibility are discussed. The results, which present that how the accelerated stress levels, censored size, hybrid censoring time and stress change time etc. affect the Jeffreys priors, are obtained. In addition, a theorem which shows there exists a relationship between single observation and multi observations for permissible priors is proved. Finally, using Metroplis with in Gibbs sampling algorithm, these factors are confirmed by computing the frequentist coverage probabilities. Springer International Publishing 2016-03-24 /pmc/articles/PMC4807206/ /pubmed/27065208 http://dx.doi.org/10.1186/s40064-016-2004-0 Text en © Zhang and Shi. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Zhang, Fode
Shi, Yimin
Permissible noninformative priors for the accelerated life test model with censored data
title Permissible noninformative priors for the accelerated life test model with censored data
title_full Permissible noninformative priors for the accelerated life test model with censored data
title_fullStr Permissible noninformative priors for the accelerated life test model with censored data
title_full_unstemmed Permissible noninformative priors for the accelerated life test model with censored data
title_short Permissible noninformative priors for the accelerated life test model with censored data
title_sort permissible noninformative priors for the accelerated life test model with censored data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4807206/
https://www.ncbi.nlm.nih.gov/pubmed/27065208
http://dx.doi.org/10.1186/s40064-016-2004-0
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