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	Semiparametric Bayesian inference for accelerated failure time models with errors-in-covariates and doubly censored data
	Shen, Junshan ; Li, Zhaonan ; Yu, Hanjun ; Fang, Xiangzhong
	2017
关键词	Accelerated failure time model Dirichlet process errors-in-covariates Gibbs sampling variable selection NONPARAMETRIC PROBLEMS CONFIDENCE-INTERVALS LINEAR-REGRESSION SELECTION DISTRIBUTIONS MIXTURES LASSO
英文摘要	This paper proposes a Bayesian semiparametric accelerated failure time model for doubly censored data with errors-in-covariates. The authors model the distributions of the unobserved covariates and the regression errors via the Dirichlet processes. Moreover, the authors extend the Bayesian Lasso approach to our semiparametric model for variable selection. The authors develop the Markov chain Monte Carlo strategies for posterior calculation. Simulation studies are conducted to show the performance of the proposed method. The authors also demonstrate the implementation of the method using analysis of PBC data and ACTG 175 data.; National Natural Science Foundation of China [11171007/A011103, 11171230, 11471024]; SCI(E); ARTICLE; 5; 1189-1205; 30
语种	英语
出处	万方 ; SCI ; http://d.g.wanfangdata.com.cn/Periodical_xtkxysx201705014.aspx
出版者	JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/470710]
专题	数学科学学院
推荐引用方式 GB/T 7714	Shen, Junshan,Li, Zhaonan,Yu, Hanjun,et al. Semiparametric Bayesian inference for accelerated failure time models with errors-in-covariates and doubly censored data. 2017-01-01.

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