Introduction to the Special Issue on Sparsity and Regularization Methods
Methodology
2013-01-03 v1
Abstract
Traditional statistical inference considers relatively small data sets and the corresponding theoretical analysis focuses on the asymptotic behavior of a statistical estimator when the number of samples approaches infinity. However, many data sets encountered in modern applications have dimensionality significantly larger than the number of training data available, and for such problems the classical statistical tools become inadequate. In order to analyze high-dimensional data, new statistical methodology and the corresponding theory have to be developed.
Keywords
Cite
@article{arxiv.1301.0211,
title = {Introduction to the Special Issue on Sparsity and Regularization Methods},
author = {Jon Wellner and Tong Zhang},
journal= {arXiv preprint arXiv:1301.0211},
year = {2013}
}
Comments
Published in at http://dx.doi.org/10.1214/12-STS409 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)