Statistical Inferences of Burr XII Lifetime Models under Joint Type-1 Competing Risks Samples

The problem of statistical inference under joint censoring samples has received considerable attention in the past few years. In this paper, we adopted this problem when units under the test fail with different causes of failure which is known by the competing risks model. The model is formulated un...

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Main Authors: Tahani A. Abushal, A. A. Soliman, G. A. Abd-Elmougod
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Journal of Mathematics
Online Access:http://dx.doi.org/10.1155/2021/9553617
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author Tahani A. Abushal
A. A. Soliman
G. A. Abd-Elmougod
author_facet Tahani A. Abushal
A. A. Soliman
G. A. Abd-Elmougod
author_sort Tahani A. Abushal
collection DOAJ
description The problem of statistical inference under joint censoring samples has received considerable attention in the past few years. In this paper, we adopted this problem when units under the test fail with different causes of failure which is known by the competing risks model. The model is formulated under consideration that only two independent causes of failure and the unit are collected from two lines of production and its life distributed with Burr XII lifetime distribution. So, under Type-I joint competing risks samples, we obtained the maximum likelihood (ML) and Bayes estimators. Interval estimation is discussed through asymptotic confidence interval, bootstrap confidence intervals, and Bayes credible interval. The numerical computations which described the quality of theoretical results are discussed in the forms of real data analyzed and Monte Carlo simulation study. Finally, numerical results are discussed and listed through some points as a brief comment.
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institution Kabale University
issn 2314-4785
language English
publishDate 2021-01-01
publisher Wiley
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series Journal of Mathematics
spelling doaj-art-0c6ed6345f8d4ce98fb8b9f13ee0e3bf2025-02-03T01:21:09ZengWileyJournal of Mathematics2314-47852021-01-01202110.1155/2021/9553617Statistical Inferences of Burr XII Lifetime Models under Joint Type-1 Competing Risks SamplesTahani A. Abushal0A. A. Soliman1G. A. Abd-Elmougod2Department of Mathematical ScienceDepartment of MathematicsDepartment of MathematicsThe problem of statistical inference under joint censoring samples has received considerable attention in the past few years. In this paper, we adopted this problem when units under the test fail with different causes of failure which is known by the competing risks model. The model is formulated under consideration that only two independent causes of failure and the unit are collected from two lines of production and its life distributed with Burr XII lifetime distribution. So, under Type-I joint competing risks samples, we obtained the maximum likelihood (ML) and Bayes estimators. Interval estimation is discussed through asymptotic confidence interval, bootstrap confidence intervals, and Bayes credible interval. The numerical computations which described the quality of theoretical results are discussed in the forms of real data analyzed and Monte Carlo simulation study. Finally, numerical results are discussed and listed through some points as a brief comment.http://dx.doi.org/10.1155/2021/9553617
spellingShingle Tahani A. Abushal
A. A. Soliman
G. A. Abd-Elmougod
Statistical Inferences of Burr XII Lifetime Models under Joint Type-1 Competing Risks Samples
Journal of Mathematics
title Statistical Inferences of Burr XII Lifetime Models under Joint Type-1 Competing Risks Samples
title_full Statistical Inferences of Burr XII Lifetime Models under Joint Type-1 Competing Risks Samples
title_fullStr Statistical Inferences of Burr XII Lifetime Models under Joint Type-1 Competing Risks Samples
title_full_unstemmed Statistical Inferences of Burr XII Lifetime Models under Joint Type-1 Competing Risks Samples
title_short Statistical Inferences of Burr XII Lifetime Models under Joint Type-1 Competing Risks Samples
title_sort statistical inferences of burr xii lifetime models under joint type 1 competing risks samples
url http://dx.doi.org/10.1155/2021/9553617
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