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政大機構典藏 > 商學院 > 統計學系 > 會議論文 > Item 140.119/151731

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题名:	Boosting method for length-biased and interval-censored survival data subject to high-dimensional error-prone covariates
作者:	陳立榜 Chen, Li-Pang
贡献者:	統計系
日期:	2024-06
上传时间:	2024-06-12 15:36:17 (UTC+8)
摘要:	In this talk, we consider the length-biased and partly interval-censored data, whose challenges primarily come from biased sampling and interfere induced by interval censoring. Unlike existing methods that focus on low-dimensional data and assume the covariates to be precisely measured, sometimes researchers may encounter high-dimensional data subject to measurement error, which are ubiquitous in applications and make estimation unreliable. To address those challenges, we explore a valid inference method for handling high-dimensional length-biased and interval-censored survival data with measurement error in covariates under the accelerated failure time model. We primarily employ the SIMEX method to correct for measurement error effects and propose the boosting procedure to do variable selection and estimation. The proposed method is able to handle the case that the dimension of covariates is larger than the sample size and enjoys appealing features that the distributions of the covariates are left unspecified.
關聯:	ICSA 2024 Applied Statistics Symposium, International Chinese Statistical Association (ICSA)
数据类型:	conference
显示于类别:	[統計學系] 會議論文

文件中的档案:

档案	描述	大小	格式	浏览次数
0617.pdf		314Kb	Adobe PDF	103	检视/开启

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