HIGH-DIMENSIONAL ADDITIVE MODELING

• 成果类型: Article
• 署名作者: Meier, Lukas; van de Geer, Sara; Buehlmann, Peter
• 署名单位: Swiss Federal Institutes of Technology Domain; ETH Zurich
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/09-AOS692
• 发表日期: 2009
• 页码: 3779-3821
• 关键词: dantzig selector; Lasso; regression; sparsity

摘要

We propose a new sparsity-smoothness penalty for high-dimensional generalized additive models. The combination of sparsity and smoothness is crucial for mathematical theory as well as performance for finite-sample data. We present a computationally efficient algorithm, with provable numerical convergence properties, for optimizing the penalized likelihood. Furthermore, we provide oracle results which yield asymptotic optimality of our estimator for high dimensional but sparse additive models. Finally, an adaptive version of Our sparsity-smoothness penalized approach yields large additional performance gains.