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Non-linear failure rate : a Bayes study using Hamiltonian Monte Carlo simulation.

Thach, T.T. and Bris, R. and Volf, P. and Coolen, F.P.A. (2020) 'Non-linear failure rate : a Bayes study using Hamiltonian Monte Carlo simulation.', International journal of approximate reasoning., 123 . pp. 55-76.

Abstract

A generalization of the linear failure rate called non-linear failure rate is introduced, analyzed, and applied to real data sets for both censored and uncensored data. The Hamiltonian Monte Carlo and cross-entropy methods have been exploited to empower the traditional methods of statistical estimation. We have obtained the Bayes estimators of parameters and reliability characteristics using Hamiltonian Monte Carlo and these estimators are considered under both symmetric and asymmetric loss functions. Additionally, the maximum likelihood estimators of parameters are obtained by using the cross-entropy method to optimize the log-likelihood function. The superiority of the proposed model and estimation procedures are demonstrated on real data sets adopted from references.

Item Type:Article
Full text:(AM) Accepted Manuscript
Available under License - Creative Commons Attribution Non-commercial No Derivatives.
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1016/j.ijar.2020.04.007
Publisher statement:© 2020 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Date accepted:07 April 2020
Date deposited:14 April 2020
Date of first online publication:28 May 2020
Date first made open access:28 May 2021

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