COORDINATE-INDEPENDENT SPARSE SUFFICIENT DIMENSION REDUCTION AND VARIABLE SELECTION

• 成果类型: Article
• 署名作者: Chen, Xin; Zou, Changliang; Cook, R. Dennis
• 署名单位: Syracuse University; Nankai University; Nankai University; University of Minnesota System; University of Minnesota Twin Cities
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/10-AOS826
• 发表日期: 2010
• 页码: 3696-3723
• 关键词: principal hessian directions; Sliced Inverse Regression; likelihood; asymptotics; components; algorithms

摘要

Sufficient dimension reduction (SDR) in regression, which reduces the dimension by replacing original predictors with a minimal set of their linear combinations without loss of information, is very helpful when the number of predictors is large. The standard SDR methods suffer because the estimated linear combinations usually consist of all original predictors, making it difficult to interpret. In this paper, we propose a unified method-coordinate-independent sparse estimation (CISE)-that can simultaneously achieve sparse sufficient dimension reduction and screen out irrelevant and redundant variables efficiently. CISE is subspace oriented in the sense that it incorporates a coordinate-independent penalty term with a broad series of model-based and model-free SDR approaches. This results in a Grassmann manifold optimization problem and a fast algorithm is suggested. Under mild conditions, based on manifold theories and techniques, it can be shown that CISE would perform asymptotically as well as if the true irrelevant predictors were known, which is referred to as the oracle property. Simulation studies and a real-data example demonstrate the effectiveness and efficiency of the proposed approach.