Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/1708
Title: On estimation of P(Y < X) for inverse Pareto distribution based on progressively first failure censored data
Authors: Alharb, R
Garg, R
Kumar, I
Kumari, A
Issue Date: Nov-2023
Abstract: The stress-strength reliability (SSR) model ϕ = P(Y < X) is used in numerous disciplines like reliability engineering, quality control, medical studies, and many more to assess the strength and stresses of the systems. Here, we assume X and Y both are independent ran dom variables of progressively first failure censored (PFFC) data following inverse Pareto distribution (IPD) as stress and strength, respectively. This article deals with the estimation of SSR from both classical and Bayesian paradigms. In the case of a classical point of view, the SSR is computed using two estimation methods: maximum product spacing (MPS) and maximum likelihood (ML) estimators. Also, derived interval estimates of SSR based on ML estimate. The Bayes estimate of SSR is computed using the Markov chain Monte Carlo (MCMC) approximation procedure with a squared error loss function (SELF) based on gamma informative priors for the Bayesian paradigm. To demonstrate the relevance of the different estimates and the censoring schemes, an extensive simulation study and two pairs of real-data applications are discussed.
URI: http://hdl.handle.net/123456789/1708
Appears in Collections:School of Basic Sciences

Files in This Item:
File Description SizeFormat 
On_estimation_of_PY_X_for_inverse_Pareto_distribut.pdf1.24 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.