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Variable selection and structure estimation for ultrahigh-dimensional additive hazards models
The Canadian Journal of Statistics ( IF 0.6 ) Pub Date : 2021-01-21 , DOI: 10.1002/cjs.11593 Li Liu 1 , Yanyan Liu 1 , Feng Su 2 , Xingqiu Zhao 3
The Canadian Journal of Statistics ( IF 0.6 ) Pub Date : 2021-01-21 , DOI: 10.1002/cjs.11593 Li Liu 1 , Yanyan Liu 1 , Feng Su 2 , Xingqiu Zhao 3
Affiliation
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.
中文翻译:
超高维可加性风险模型的变量选择和结构估计
我们开发了一类基于惩罚筛最小二乘法的正则化方法,用于高维加性风险回归模型中的同步模型追踪、变量选择和估计。在稀疏超高维模型的框架下,所提出的估计量的渐近特性包括结构识别一致性和预言机变量选择。通过应用分块优化下降算法可以有效地实现计算过程。模拟研究证明了所提出方法的性能,并提供了原发性胆汁性肝硬化数据分析以供说明。
更新日期:2021-01-21
中文翻译:
超高维可加性风险模型的变量选择和结构估计
我们开发了一类基于惩罚筛最小二乘法的正则化方法,用于高维加性风险回归模型中的同步模型追踪、变量选择和估计。在稀疏超高维模型的框架下,所提出的估计量的渐近特性包括结构识别一致性和预言机变量选择。通过应用分块优化下降算法可以有效地实现计算过程。模拟研究证明了所提出方法的性能,并提供了原发性胆汁性肝硬化数据分析以供说明。