Parametric and Interval Estimation Under Step-Stress Partially Accelerated Life Tests Using Adaptive Type-II Progressive Hybrid Censoring

Intekhab Alam () and Aquil Ahmed
Additional contact information
Intekhab Alam: Aligarh Muslim University
Aquil Ahmed: Aligarh Muslim University

Annals of Data Science, 2023, vol. 10, issue 2, No 8, 453 pages

Abstract: Abstract In this paper, the likelihood estimation of model parameters and acceleration factor are considered under step-stress partially accelerated life test using adaptive type-II progressive hybrid censoring scheme, when the lifetime of the test units follows Exponentiated Pareto distribution. The numerical values of Maximum likelihood estimators are obtained using the Newton–Raphson technique. The performance of model parameters and acceleration factor in terms of mean square errors and biases are evaluated using the Monte-Carlo simulation technique.

Keywords: Step-stress partially accelerated life test; Adaptive type-II progressive hybrid censoring; Exponentiated pareto distribution; Newton–raphson technique; Monte-carlo simulation (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40745-020-00249-1 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:aodasc:v:10:y:2023:i:2:d:10.1007_s40745-020-00249-1

Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/40745

DOI: 10.1007/s40745-020-00249-1

Access Statistics for this article

Annals of Data Science is currently edited by Yong Shi

More articles in Annals of Data Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().