@article{a0844b0ac9a04adab086421b1ec46bd4,
title = "Variable selection and structure estimation for ultrahigh-dimensional additive hazards models",
abstract = "We develop a class of regularization methods based on the penalized sieve least squares for simultaneous model pursuit, variable selection, and estimation in high-dimensional additive hazards regression models. In the framework of sparse ultrahigh-dimensional models, the asymptotic properties of the proposed estimators include structure identification consistency and oracle variable selection. The computational process can be efficiently implemented by applying the blockwise majorization descent algorithm. Simulation studies demonstrate the performance of the proposed methodology, and the primary biliary cirrhosis data analysis is provided for illustration.",
keywords = "Additive hazards regression, model pursuit, penalized sieve least squares, ultrahigh-dimensional censored data, variable selection",
author = "Li Liu and Yanyan Liu and Feng Su and Xingqiu Zhao",
note = "Funding Information: The authors thank Editor-in-Chief Professor Fang Yao, the Associate Editor, and the two reviewers for their constructive and insightful comments and suggestions that greatly improved this article. Li Liu's research is partly supported by the National Natural Science Foundation of China (No. 11971362). Yanyan Liu and Feng Su's research is partly supported by the National Natural Science Foundation of China (No. 115771263). Zhao's research is partly supported by the National Natural Science Foundation of China (No. 11771366) and the Research Grant Council of Hong Kong (15301218, 15303319). Funding Information: The authors thank Editor‐in‐Chief Professor Fang Yao, the Associate Editor, and the two reviewers for their constructive and insightful comments and suggestions that greatly improved this article. Li Liu's research is partly supported by the National Natural Science Foundation of China (No. 11971362). Yanyan Liu and Feng Su's research is partly supported by the National Natural Science Foundation of China (No. 115771263). Zhao's research is partly supported by the National Natural Science Foundation of China (No. 11771366) and the Research Grant Council of Hong Kong (15301218, 15303319). Publisher Copyright: {\textcopyright} 2021 Statistical Society of Canada",
year = "2021",
month = sep,
doi = "10.1002/cjs.11593",
language = "English",
volume = "49",
pages = "826--852",
journal = "Canadian Journal of Statistics",
issn = "0319-5724",
publisher = "Wiley-Blackwell",
number = "3",
}