Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93890
Title: | Joint model-free feature screening for ultra-high dimensional semi-competing risks data |
Authors: | Lu, S Chen, X Xu, S Liu, C |
Issue Date: | Jul-2020 |
Source: | Computational statistics and data analysis, July 2020, v. 147, 106942 |
Abstract: | High-dimensional semi-competing risks data consisting of two probably correlated events, namely terminal event and non-terminal event, arise commonly in many biomedical studies. However, the corresponding statistical analysis is rarely investigated. A joint model-free feature screening procedure for both terminal and non-terminal events is proposed, which could allow the associated covariates to be in an ultra-high dimensional feature space. The joint screening utility is constructed from distance correlation between each predictor's survival function and joint survival function of terminal and non-terminal events. Under rather mild technical assumptions, it is demonstrated that the proposed joint feature screening procedure enjoys sure screening and consistency in ranking properties. An adaptive threshold rule is further suggested to simultaneously identify important covariates and determine number of these covariates. Extensive numerical studies are conducted to examine the finite-sample performance of the proposed methods. Lastly, the suggested joint feature screening procedure is illustrated through a real example. |
Keywords: | Clayton copula Distance correlation Feature screening Semi-competing risks data Ultra-high dimensionality |
Publisher: | Elsevier |
Journal: | Computational statistics and data analysis |
EISSN: | 0167-9473 |
DOI: | 10.1016/j.csda.2020.106942 |
Rights: | © 2020 Elsevier B.V. All rights reserved. © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ The following publication Lu, S., Chen, X., Xu, S., & Liu, C. (2020). Joint model-free feature screening for ultra-high dimensional semi-competing risks data. Computational Statistics & Data Analysis, 147, 106942 is available at https://doi.org/10.1016/j.csda.2020.106942 |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Xu_Joint_Model-Free_Feature.pdf | Pre-Published version | 798.02 kB | Adobe PDF | View/Open |
Page views
73
Last Week
1
1
Last month
Citations as of Oct 20, 2024
Downloads
62
Citations as of Oct 20, 2024
SCOPUSTM
Citations
3
Citations as of Oct 24, 2024
WEB OF SCIENCETM
Citations
3
Citations as of Oct 10, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.