A note on the one-step estimator for ultrahigh dimensionality

作者：Wang, Mingqiu; Wang, Xiuli; Wang, Xiaoguang

来源：Journal of Computational and Applied Mathematics, 2014, 260: 91-98.

DOI：10.1016/j.cam.2013.09.037

• Cross validation
• High-dimensional data
• Model selection consistency
• One-step estimator
• Partial orthogonality

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.

• 出版日期2014-4
• 单位大连理工大学; 曲阜师范大学