Automatic structure recovery for additive models

• 成果类型: Article
• 署名作者: Wu, Yichao; Stefanski, Leonard A.
• 署名单位: North Carolina State University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asu070
• 发表日期: 2015
• 页码: 381395
• 关键词: VARIABLE SELECTION; adaptive lasso; likelihood

摘要

We propose an automatic structure recovery method for additive models, based on a backfitting algorithm coupled with local polynomial smoothing, in conjunction with a new kernel-based variable selection strategy. Our method produces estimates of the set of noise predictors, the sets of predictors that contribute polynomially at different degrees up to a specified degree M, and the set of predictors that contribute beyond polynomially of degree M. We prove consistency of the proposed method, and describe an extension to partially linear models. Finite-sample performance of the method is illustrated via Monte Carlo studies and a real-data example.