Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6416020 | Linear Algebra and its Applications | 2016 | 16 Pages |
Abstract
Consider a nÃp random matrix X with i.i.d. rows. We show that the least eigenvalue of nâ1Xâ¤X is bounded away from zero with high probability when p/n⩽y for some fixed y in (0,1) and normalized orthogonal projections of rows are not too close to zero. The principal difference from the previous results is that y can be arbitrarily close to one. Our results cover many cases of interest in high-dimensional statistics and random matrix theory.
Related Topics
Physical Sciences and Engineering
Mathematics
Algebra and Number Theory
Authors
Pavel Yaskov,