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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/118258

    Title: Reliability inference for a multicomponent stress-strength model based on Kumaraswamy distribution
    Authors: Liang Wang;Sanku Dey;Yogesh Mani Tripathi;Shuo-Jye Wu
    Keywords: Multicomponent stress–strength model;Kumaraswamy distribution;Maximum likelihood estimation;Generalized pivotal quantity;Asymptotic theory;Bootstrap interval
    Date: 2020-10
    Issue Date: 2020-03-12 12:10:54 (UTC+8)
    Abstract: In this paper, inference for a multicomponent stress–strength (MSS) model is studied under censored data. When both latent strength and stress random variables follow Kumaraswamy distributions with common shape parameters, the maximum likelihood estimate of MSS reliability is established and associated approximate confidence interval is constructed using the asymptotic distribution theory and delta method. Moreover, pivotal quantities based generalized point and confidence interval estimates are presented for the MSS reliability. Furthermore, likelihood and generalized pivotal based estimates are also presented when the strength and stress variables have unequal shape parameters. For complementary and comparison, bootstrap confidence intervals are provided as well under common and unequal parameter cases. In addition, to compare the equivalence between strength and stress shape parameters, the likelihood ratio test for hypothesis of interest is also discussed. Finally, simulation study and a real data example are provided to investigate the performance of proposed procedures.
    Relation: Journal of Computational and Applied Mathematics 376, 112823
    DOI: 10.1016/j.cam.2020.112823
    Appears in Collections:[統計學系暨研究所] 期刊論文

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