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政大機構典藏 > 商學院 > 統計學系 > 期刊論文 > Item 140.119/142028

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/142028

Title:	Ultrahigh-dimensional sufficient dimension reduction for censored data with measurement error in covariates
Authors:	陳立榜 Chen, Li-Pang
Contributors:	統計系
Keywords:	Cumulative mean estimation;dimension reduction;distance correlation;feature screening;measurement error;survival data;ultrahigh-dimension
Date:	2020-11
Issue Date:	2022-09-21 11:46:13 (UTC+8)
Abstract:	In this paper, we consider the ultrahigh-dimensional sufficient dimension reduction (SDR) for censored data and measurement error in covariates. We first propose the feature screening procedure based on censored data and the covariates subject to measurement error. With the suitable correction of mismeasurement, the error-contaminated variables detected by the proposed feature screening procedure are the same as the truly important variables. Based on the selected active variables, we develop the SDR method to estimate the central subspace and the structural dimension with both censored data and measurement error incorporated. The theoretical results of the proposed method are established. Simulation studies are reported to assess the performance of the proposed method. The proposed method is implemented to NKI breast cancer data.
Relation:	Journal of Applied Statistics, Vol.49, No.5, pp.1154-1178
Data Type:	article
DOI 連結:	https://doi.org/10.1080/02664763.2020.1856352
DOI:	10.1080/02664763.2020.1856352
Appears in Collections:	[統計學系] 期刊論文

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