432
Views
15
CrossRef citations to date
0
Altmetric
Research Article

Estimation of stress–strength reliability for generalized Maxwell failure distribution under progressive first failure censoring

ORCID Icon, ORCID Icon & ORCID Icon
Pages 1366-1393 | Received 11 Nov 2019, Accepted 24 Nov 2020, Published online: 28 Jan 2021
 

Abstract

In this article, the estimation of the stress–strength reliability function δ=P(X>Y) for generalized Maxwell failure distribution is considered. The Maximum-likelihood and Bayesian estimators for δ based on progressive first failure censoring scheme are developed. The asymptotic confidence, bootstrap p, bootstrap t, Bayesian credible and HPD credible intervals for the stress–strength reliability are derived. A Monte Carlo simulation study is provided for comparing different estimators and various censoring schemes. Finally, a real data study is carried out which illustrate the proposed estimation methods and censoring schemes.

Mathematics subject classifications:

Acknowledgments

Authors are grateful to the Editor and anonymous referees for their valuable suggestions, which greatly improved this manuscript. The first author, Mr. Shubham Saini is grateful to the Council of Scientific and Industrial Research (CSIR), Ministry of Science and Technology, Government of India for their financial support in the form of Junior Research fellowship (09/045(1614)/2018-EMR-I).

Disclosure statement

No potential conflict of interest was reported by the authors.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 1,209.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.