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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/58762

    Title: Nonparametric maximum likelihood estimator in a semiparametric mixture model for competing risks data
    Authors: 溫啟仲;Chang, I-SHOU;Hsiung, CHAO A.;Wu, YUH-JENN;Yang, CHE-CHI
    Contributors: 淡江大學數學學系
    Date: 2007-12-01
    Issue Date: 2011-10-01 21:09:20 (UTC+8)
    Abstract: This paper describes our studies on non-parametric maximum-likelihood estimators in a semiparametric mixture model for competing-risks data, in which proportional hazards models are specified for failure time models conditional on cause and a multinomial model is specified for the marginal distribution of cause conditional on covariates. We provide a verifiable identifiability condition and, based on it, establish an asymptotic profile likelihood theory for this model. We also provide efficient algorithms for the computation of the non-parametric maximum-likelihood estimate and its asymptotic variance. The success of this method is demonstrated in simulation studies and in the analysis of Taiwan severe acute respiratory syndrome data.
    Relation: Scandinavian Journal of Statistics 34(4), pp.870-895
    DOI: 10.1111/j.1467-9469.2007.00567.x
    Appears in Collections:[Graduate Institute & Department of Mathematics] Journal Article

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