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Variable selection and structure estimation for ultrahigh‐dimensional additive hazards models
Author(s) -
Liu Li,
Liu Yanyan,
Su Feng,
Zhao Xingqiu
Publication year - 2021
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11593
Subject(s) - feature selection , model selection , estimator , computer science , lasso (programming language) , consistency (knowledge bases) , mathematical optimization , regularization (linguistics) , majorization , algorithm , mathematics , artificial intelligence , statistics , combinatorics , world wide web
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.