Open Access
2022 Sufficient dimension reduction for survival data analysis with error-prone variables
Li-Pang Chen, Grace Y. Yi
Author Affiliations +
Electron. J. Statist. 16(1): 2082-2123 (2022). DOI: 10.1214/22-EJS1977


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.


This research was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and partially supported by a Collaborative Research Project of the Canadian Statistical Sciences Institute. Yi is Canada Research Chair in Data Science (Tier 1). Her research was undertaken, in part, thanks to funding from the Canada Research Chairs program.


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Li-Pang Chen. Grace Y. Yi. "Sufficient dimension reduction for survival data analysis with error-prone variables." Electron. J. Statist. 16 (1) 2082 - 2123, 2022.


Received: 1 February 2021; Published: 2022
First available in Project Euclid: 25 March 2022

MathSciNet: MR4399167
zbMATH: 07524970
Digital Object Identifier: 10.1214/22-EJS1977

Keywords: cross-validation , Dimension reduction , error-prone variable , right-censoring , semiparamtric estimation

Vol.16 • No. 1 • 2022
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