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Oracle inequalities for the lasso in the Cox model

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Huang, Jian, Sun, Tingni, Ying, Zhiliang, Yu, Yi and Zhang, Cun-Hui (2013) Oracle inequalities for the lasso in the Cox model. The Annals of Statistics, 41 (3). pp. 1142-1165. doi:10.1214/13-AOS1098 ISSN 0090-5364.

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Official URL: http://dx.doi.org/10.1214/13-AOS1098

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Abstract

We study the absolute penalized maximum partial likelihood estimator in sparse, high-dimensional Cox proportional hazards regression models where the number of time-dependent covariates can be larger than the sample size. We establish oracle inequalities based on natural extensions of the compatibility and cone invertibility factors of the Hessian matrix at the true regression coefficients. Similar results based on an extension of the restricted eigenvalue can be also proved by our method. However, the presented oracle inequalities are sharper since the compatibility and cone invertibility factors are always greater than the corresponding restricted eigenvalue. In the Cox regression model, the Hessian matrix is based on time-dependent covariates in censored risk sets, so that the compatibility and cone invertibility factors, and the restricted eigenvalue as well, are random variables even when they are evaluated for the Hessian at the true regression coefficients. Under mild conditions, we prove that these quantities are bounded from below by positive constants for time-dependent covariates, including cases where the number of covariates is of greater order than the sample size. Consequently, the compatibility and cone invertibility factors can be treated as positive constants in our oracle inequalities.

Item Type: Journal Article
Divisions: Faculty of Science, Engineering and Medicine > Science > Statistics
Journal or Publication Title: The Annals of Statistics
Publisher: Institute of Mathematical Statistics
ISSN: 0090-5364
Official Date: 13 June 2013
Dates:
DateEvent
13 June 2013Published
Volume: 41
Number: 3
Page Range: pp. 1142-1165
DOI: 10.1214/13-AOS1098
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)

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