Inference on stress-strength reliability for the two-parameter exponential distribution based on generalized order statistics

Ali Akbar Jafari and Saeede Bafekri

Mathematical Population Studies, 2021, vol. 28, issue 4, 201-227

Abstract: Stress-strength reliability is a measure to compare the lifetimes of two systems. It is inferred for the two-parameter exponential distribution using generalized order statistics first without constraint on the location and scale parameters, second when the scale parameters are equal. A generalized confidence interval, bootstrap confidence intervals, a Bayesian interval, and a highest posterior density interval are computed for the stress-strength parameter. A Monte Carlo simulation shows that generalized confidence intervals provide more accurate average lengths of confidence intervals and higher probabilities to contain the true value of the parameter. Application: Confidence intervals for the time to remission of 20 leukemic patients treated with one of two drugs are approximately the same in most generalized statistical models. In addition, the time to remission for patients with the first drug is tested to be shorter than for patients with the second drug.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/08898480.2021.1872230 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:mpopst:v:28:y:2021:i:4:p:201-227

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GMPS20

DOI: 10.1080/08898480.2021.1872230

Access Statistics for this article

Mathematical Population Studies is currently edited by Prof. Noel Bonneuil, Annick Lesne, Tomasz Zadlo, Malay Ghosh and Ezio Venturino

More articles in Mathematical Population Studies from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().