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    题名: Ultrahigh-dimensional sufficient dimension reduction for censored data with measurement error in covariates
    作者: 陳立榜
    Chen, Li-Pang
    贡献者: 統計系
    关键词: Cumulative mean estimation;dimension reduction;distance correlation;feature screening;measurement error;survival data;ultrahigh-dimension
    日期: 2020-11
    上传时间: 2022-09-21 11:46:13 (UTC+8)
    摘要: 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.
    關聯: Journal of Applied Statistics, Vol.49, No.5, pp.1154-1178
    数据类型: article
    DOI 連結: https://doi.org/10.1080/02664763.2020.1856352
    DOI: 10.1080/02664763.2020.1856352
    显示于类别:[統計學系] 期刊論文

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