The reliability inference for multicomponent stress–strength model under the Burr X distribution
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Date
Authors
Lio, Yuhlong
Chen, Ding-Geng (Din)
Tsai, Tzong-Ru
Wang, Liang
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Abstract
The reliability of the multicomponent stress–strength system was investigated under
the two-parameter Burr X distribution model. Based on the structure of the system, the type II
censored sample of strength and random sample of stress were obtained for the study. The maximum
likelihood estimators were established by utilizing the type II censored Burr X distributed strength
and complete random stress data sets collected from the multicomponent system. Two related
approximate confidence intervals were achieved by utilizing the delta method under the asymptotic
normal distribution theory and parametric bootstrap procedure. Meanwhile, point and confidence
interval estimators based on alternative generalized pivotal quantities were derived. Furthermore, a
likelihood ratio test to infer the equality of both scalar parameters is provided. Finally, a practical
example is provided for illustration.
Description
DATA AVAILABILITY STATEMENT: Complete monthly water capacity data for the Shasta Reservoir from 1981 to 1985 are given in Appendix G. Section 6 includes the observed complete strength and stress data sets.
Keywords
Multicomponent stress–strength model, Burr X distribution, Generalized pivotal estimation, Asymptotic theory, Maximum likelihood estimator (MLE), SDG-04: Quality education, SDG-09: Industry, innovation and infrastructure
Sustainable Development Goals
SDG-04:Quality Education
SDG-09: Industry, innovation and infrastructure
SDG-09: Industry, innovation and infrastructure
Citation
Lio, Y.; Chen, D.-G.; Tsai, T.-R.; Wang, L. The Reliability Inference for Multicomponent
Stress–Strength Model under the Burr X Distribution. AppliedMath 2024, 4,
394–426. https://doi.org/10.3390/appliedmath4010021.
