Release Time:2019-03-09  Hits:

Indexed by: Journal Article

Date of Publication: 2014-04-01

Journal: JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

Included Journals: EI、SCIE

Volume: 260

Page Number: 91-98

ISSN: 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.