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 illustra-tion.
Original language | English |
---|---|
Pages (from-to) | 748-772 |
Number of pages | 25 |
Journal | Electronic Journal of Statistics |
Volume | 15 |
Issue number | 1 |
DOIs | |
Publication status | E-pub ahead of print - 21 Jan 2021 |
Keywords
- Additive hazards model
- Composite penalty
- High dimension
- Local coordinate descent algorithm
- Oracle property
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty