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Annals of Data Science
Erschienen in:

02.07.2022

Inference on a Multicomponent Stress-Strength Model Based on Unit-Burr III Distributions

verfasst von: Devendra Pratap Singh, Mayank Kumar Jha, Yogesh Mani Tripathi, Liang Wang

Erschienen in: Annals of Data Science | Ausgabe 5/2023

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Abstract

We make inference for a multicomponent stress-strength (MS) model under type-II censoring. It is assumed that lifetimes of strength and stress components follow unit Burr III distributions. Maximum likelihood estimator of MS parameter is obtained under a common shape parameter and in sequel approximate confidence intervals are constructed. Pivotal quantities based inference is also discussed. The case of unequal common parameters is considered as well and various inferences are derived. In addition, likelihood ratio tests are constructed to test the equivalence of parameters of interest. We conduct a simulation study to examine the behavior of proposed estimation procedures. A real data set is also analyzed from application viewpoint.
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Metadaten
Titel
Inference on a Multicomponent Stress-Strength Model Based on Unit-Burr III Distributions
verfasst von
Devendra Pratap Singh
Mayank Kumar Jha
Yogesh Mani Tripathi
Liang Wang
Publikationsdatum
02.07.2022
Verlag
Springer Berlin Heidelberg
Erschienen in
Annals of Data Science / Ausgabe 5/2023
Print ISSN: 2198-5804
Elektronische ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-022-00429-1

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