Journal article
Projection-based and cross-validated estimation in high-dimensional Cox model
Scandinavian journal of statistics, Vol.49(1), pp.353-372
03/01/2022
DOI: 10.1111/sjos.12515
Abstract
We propose a projection-based cross-validation method for estimating a low-dimensional parameter in the presence of a high-dimensional nuisance parameter in the Cox regression model. We show that the proposed estimator is asymptotically normal, which enables us to conduct hypothesis test for the parameter of interest with high-dimensional nuisance parameters. Three decision rules are presented to avoid the influence of random splitting of samples. Simulation studies indicate that our method is more powerful than that of Fang et al. (2017, JRSSB) when the coefficients of predictors are high-dimensional and not very sparse. As an illustrative example, we apply our procedure to a breast cancer study.
Details
- Title: Subtitle
- Projection-based and cross-validated estimation in high-dimensional Cox model
- Creators
- Haixiang Zhang - Tianjin UniversityJian Huang - University of IowaLiuquan Sun - Chinese Academy of Sciences
- Resource Type
- Journal article
- Publication Details
- Scandinavian journal of statistics, Vol.49(1), pp.353-372
- Publisher
- Wiley
- DOI
- 10.1111/sjos.12515
- ISSN
- 0303-6898
- eISSN
- 1467-9469
- Number of pages
- 20
- Grant note
- 11771431; 11690015; 11926341 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) DMS-1916199 / NSF; National Science Foundation (NSF)
- Language
- English
- Date published
- 03/01/2022
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257719302771
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