Estimation of the system reliability for generalized inverse Lindley distribution based on different sampling designs

Akgul F. G., Yu K., Şenoğlu B.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, vol.50, no.7, pp.1532-1546, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 50 Issue: 7
  • Publication Date: 2021
  • Doi Number: 10.1080/03610926.2019.1705977
  • Journal Name: COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Business Source Elite, Business Source Premier, CAB Abstracts, Compendex, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.1532-1546
  • Keywords: Stress-strength reliability, sampling designs, maximum likelihood, imperfect ranking, efficiency, MAXIMUM-LIKELIHOOD ESTIMATORS, STRESS-STRENGTH RELIABILITY, PARAMETER
  • Karadeniz Technical University Affiliated: No

Abstract

In this paper, we are interested in estimating stress-strength reliability when the distributions of stress and strength are independent generalized inverse Lindley (GIL) under different sampling designs, namely, simple random sampling (SRS), ranked set sampling (RSS) and percentile ranked set sampling (PRSS). In the context of parameter estimation, we use maximum likelihood (ML) methodology. The performance of the ML estimators of stress-strength reliability based on SRS, RSS and PRSS are compared via a Monte-Carlo simulation study for different parameter settings and sample sizes under the assumptions of perfect and imperfect ranking, respectively. At the end of the study, the insulin resistance data set is analyzed to implement the proposed methodologies.