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Indexed by:期刊论文

Date of Publication:2014-04-01

Journal:JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

Included Journals:SCIE、EI

Volume:260

Page Number:91-98

ISSN No.:0377-0427

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

Abstract: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.