Estimation of distribution function using L ranked set sampling and robust extreme ranked set sampling with application to reliability

Mohamed S. Abdallah (), Amer I. Al-Omari (), Naif Alotaibi (), Ghadah A. Alomani () and A. S. Al-Moisheer
Additional contact information
Mohamed S. Abdallah: Aswan University
Amer I. Al-Omari: Al al-Bayt University
Naif Alotaibi: Imam Mohammad Ibn Saud, Islamic University
Ghadah A. Alomani: Princess Nourah bint Abdulrahman University
A. S. Al-Moisheer: Jouf University

Computational Statistics, 2022, vol. 37, issue 5, No 11, 2333-2362

Abstract: Abstract This paper suggests new estimators for cumulative distribution function (CDF) using L ranked set sampling (LRSS) and robust extreme ranked set sampling (RERSS) methods. The proposed estimators are deduced based on maximum likelihood estimation method and its asymptotic properties are theoretically investigated. Comparison study has been made to demonstrate the efficiency of the proposed estimators. It is found that when the data contain outliers, the proposed estimators are less sensitive and have a satisfied behavior compared to their analog in ranked set sampling as the population CDF is far away from the boundaries. Motivated by this efficiency gain, we provided new estimators for system reliability $$\mathcal{R}$$ R = $$P(Y>X)$$ P ( Y > X ) using LRSS and RERSS. Through a Monte Carlo simulation study, the performance of the introduced reliability estimators is also examined. A real data set is analyzed to illustrate the applicability of the proposed various estimators.

Keywords: L ranked set sampling; Robust extreme ranked set sampling; Distribution function; Stress-strength; Reliability; Ranking errors (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-022-01201-y 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:compst:v:37:y:2022:i:5:d:10.1007_s00180-022-01201-y

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-022-01201-y

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

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