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• W2220492787 abstract "A flexible Bayesian semiparametric accelerated failure time (AFT) model is proposed for analyzing arbitrarily censored survival data with covariates subject to measurement error. Specifically, the baseline error distribution in the AFT model is nonparametrically modeled as a Dirichlet process mixture of normals. Classical measurement error models are imposed for covariates subject to measurement error. An efficient and easy-to-implement Gibbs sampler, based on the stick-breaking formulation of the Dirichlet process combined with the techniques of retrospective and slice sampling, is developed for the posterior calculation. An extensive simulation study is conducted to illustrate the advantages of our approach." @default.
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• W2220492787 date "2016-10-21" @default.
• W2220492787 modified "2023-10-18" @default.
• W2220492787 title "A Bayesian semiparametric accelerated failure time model for arbitrarily censored data with covariates subject to measurement error" @default.
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• W2220492787 doi "https://doi.org/10.1080/03610918.2014.977919" @default.
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