Optimal prediction regions of future lifetimes under Type-II censored samples

Mohammad Z. Raqab (), S. F. Bagheri, A. Asgharzadeh and Ahmad N. Alothman
Additional contact information
Mohammad Z. Raqab: Kuwait University
S. F. Bagheri: University of Mazandaran
A. Asgharzadeh: University of Mazandaran
Ahmad N. Alothman: Kuwait University

Statistical Papers, 2025, vol. 66, issue 5, No 11, 25 pages

Abstract: Abstract Based on a Type-II censored data from exponential distribution, Bagheri et al. (IEEE Trans Reliab 71(1):100–110, 2022) introduced the joint prediction of future failure times based on Type-II censored data from the exponential distribution. In fact, they developed pivotal quantities for obtaining prediction regions of two future failure times using the stochastic independence of the spacings of increments of order statistics along with a scaling transformation. In this work, we consider the same problem of joint prediction where the prediction likelihood function is used to produce pivotal quantities for obtaining prediction region of two failure times. Based on these pivotal quantities, the optimal prediction regions of the joint failure times are derived. A Monte Carlo simulation study is performed to assess the so developed prediction regions and compare them with ones obtained by Bagheri et al. (IEEE Trans Reliab 71(1):100–110, 2022). Through simulation, it is evident that our joint prediction regions are highly competitive efficiencies compared to their existing counterparts. Further, two real life data sets are analyzed to explain our procedures presented.

Keywords: Constrained optimization; Exponential distribution; Monte Carlo simulation; Pivotal quantity; Prediction region; Prediction likelihood function; Type-II censoring; 62N01; 62N02 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00362-025-01729-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:stpapr:v:66:y:2025:i:5:d:10.1007_s00362-025-01729-3

Ordering information: This journal article can be ordered from
http://www.springer. ... business/journal/362

DOI: 10.1007/s00362-025-01729-3

Access Statistics for this article

Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller

More articles in Statistical Papers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().