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政大機構典藏 > 商學院 > 統計學系 > 期刊論文 > Item 140.119/142025

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/142025

Title:	Sufficient dimension reduction for survival data analysis with error-prone variables
Authors:	陳立榜 Chen, Li-Pang Yi, Grace Y.
Contributors:	統計系
Keywords:	cross-validation;Dimension reduction;error-prone variable;right-censoring;semiparamtric estimation
Date:	2022-03
Issue Date:	2022-09-21 11:46:03 (UTC+8)
Abstract:	Sufficient dimension reduction (SDR) is an important tool in regression analysis which reduces the dimension of covariates without losing predictive information. Several methods have been proposed to handle data with either censoring in the response or measurement error in covariates. However, little research is available to deal with data having these two features simultaneously. In this paper, we examine this problem. We start with considering the cumulative distribution function in regular settings and propose a valid SDR method to incorporate the effects of censored data and covariates measurement error. Theoretical results are established, and numerical studies are reported to assess the performance of the proposed methods.
Relation:	Electronic Journal of Statistics, Vol.16, No.1, pp.2082-2123
Data Type:	article
DOI 連結:	https://doi.org/10.1214/22-EJS1977
DOI:	10.1214/22-EJS1977
Appears in Collections:	[統計學系] 期刊論文

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