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2021 Bi-selection in the high-dimensional additive hazards regression model
Li Liu, Wen Su, Xingqiu Zhao
Electron. J. Statist. 15(1): 748-772 (2021). DOI: 10.1214/21-EJS1799

Abstract

In this article, we consider a class of regularized regression under the additive hazards model with censored survival data and propose a novel approach to achieve simultaneous group selection, variable selection, and parameter estimation for high-dimensional censored data, by combining the composite penalty and the pseudoscore. We develop a local coordinate descent (LCD) algorithm for efficient computation and subsequently establish the theoretical properties for the proposed selection methods. As a result, the selectors possess both group selection oracle property and variable selection oracle property, and thus enable us to simultaneously identify important groups and important variables within selected groups with high probability. Simulation studies demonstrate that the proposed method and LCD algorithm perform well. A real data example is provided for illustration.

Citation

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Li Liu. Wen Su. Xingqiu Zhao. "Bi-selection in the high-dimensional additive hazards regression model." Electron. J. Statist. 15 (1) 748 - 772, 2021. https://doi.org/10.1214/21-EJS1799

Information

Received: 1 July 2019; Published: 2021
First available in Project Euclid: 21 January 2021

Digital Object Identifier: 10.1214/21-EJS1799

Subjects:
Primary: 62N01, 62N02
Secondary: 62F12

JOURNAL ARTICLE
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Vol.15 • No. 1 • 2021
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