ORACLE INEQUALITIES FOR LASSO AND DANTZIG SELECTOR IN HIGH-DIMENSIONAL LINEAR REGRESSION

DOI:

https://doi.org/10.24297/jam.v9i7.2313

Keywords:

lasso, Dantzig selector, oracle inequalities, restricted eigenvalue conditions, compatibility condition, UDP condition

Abstract

During the last few years, a great deal attention has been focused on lasso and Dantzig selector in high-dimensional linear regression under a sparsity scenario, that is, when the number of variables can be much larger than the sample size. The authors [4][11][12] derived sparsity oracle inequalities of lasso and Dantzig selector for the prediction risk and bounds on the  estimation loss under a variety of assumptions. In this paper, we take the restricted eigenvalue conditions, compatibility condition and UDP condition for examples to show oracle inequalities about lasso and Dantzig selector for high-dimensional linear regression.

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Published

2014-12-31

How to Cite

ORACLE INEQUALITIES FOR LASSO AND DANTZIG SELECTOR IN HIGH-DIMENSIONAL LINEAR REGRESSION. (2014). JOURNAL OF ADVANCES IN MATHEMATICS, 9(7), 2857–2868. https://doi.org/10.24297/jam.v9i7.2313

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