Bivariate iterated Farlie–Gumbel–Morgenstern stress–strength reliability model for Rayleigh margins: Properties and estimation

• Published in: Statistical theory and related fields Vol. 8; no. 4; pp. 315 - 334
• Main Authors: Chandra, N., James, A., Domma, Filippo, Rehman, Habbiburr
• Format: Journal Article
• Language: English
• Published: Taylor & Francis Group 01.10.2024
• Subjects: dependence stress–strength; Iterated FGM; Monte-Carlo simulation; Rayleigh distribution; reliability
• ISSN: 2475-4269; 2475-4277
• DOI: 10.1080/24754269.2024.2398987

In this paper, we propose bivariate iterated Farlie–Gumbel–Morgenstern (FGM) due to [Huang and Kotz (1984). Correlation structure in iterated Farlie-Gumbel-Morgenstern distributions. Biometrika 71(3), 633–636. https://doi.org/10.2307/2336577] with Rayleigh marginals. The dependence stress–strength reliability function is derived with its important reliability characteristics. Estimates of dependence reliability parameters are obtained. We analyse the effects of dependence parameters on the reliability function. We found that the upper bound of the positive correlation coefficient is attaining to 0.41 under a single iteration with Rayleigh marginals. A comprehensive comparison between classical FGM with iterated FGM copulas is graphically examined to assess the over or under estimation of reliability with respect to α and β. We propose a two-phase estimation procedure for estimating the reliability parameters. A Monte-Carlo simulation study is conducted to assess the finite sample behaviour of the proposed reliability estimators. Finally, the proposed estimators are examined and validated with real data sets.