On estimation of P(Y < X) for inverse Pareto distribution based on progressively first failure censored data

Simple item page
dc.contributor.authorAlharb, R
dc.contributor.authorGarg, R
dc.contributor.authorKumar, I
dc.contributor.authorKumari, A
dc.date.accessioned2024-10-14T11:21:51Z
dc.date.available2024-10-14T11:21:51Z
dc.date.issued2023-11
dc.description.abstractThe 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.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/1708
dc.titleOn estimation of P(Y < X) for inverse Pareto distribution based on progressively first failure censored dataen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
On_estimation_of_PY_X_for_inverse_Pareto_distribut.pdf
Size:
1.21 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
School of Basic Sciences