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论文类型：期刊论文

发表时间：2014-04-01

发表刊物：JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

收录刊物：SCIE、EI

卷号：260

页面范围：91-98

ISSN号：0377-0427

关键字：Cross validation; High-dimensional data; Model selection consistency; One-step estimator; Partial orthogonality

摘要：The one-step estimator, covering various penalty functions, enjoys the oracle property with a good initial estimator. The initial estimator can be chosen as the least squares estimator or maximum likelihood estimator in low-dimensional settings. However, it is not available in ultrahigh dimensionality. In this paper, we study the one-step estimator with the initial estimator being marginal ordinary least squares estimates in the ultrahigh linear model. Under some appropriate conditions, we show that the one-step estimator is selection consistent. Finite sample performance of the proposed procedure is assessed by Monte Carlo simulation studies. (C) 2013 Elsevier B.V. All rights reserved.