Date of this Version
The Annals of Statistics
Professors Candès and Tao are to be congratulated for their innovative and valuable contribution to high-dimensional sparse recovery and model selection. The analysis of vast data sets now commonly arising in scientific investigations poses many statistical challenges not present in smaller scale studies. Many of these data sets exhibit sparsity where most of the data corresponds to noise and only a small fraction is of interest. The needs of this research have excited much interest in the statistical community. In particular, high-dimensional model selection has attracted much recent attention and has become a central topic in statistics. The main difficulty of such a problem comes from collinearity between the predictor variables. It is clear from the geometric point of view that the collinearity increases as the dimensionality grows.
Cai, T., & Lv, J. (2007). Discussion: The Dantzig Selector: Statistical Estimation When p is Much Larger Than n. The Annals of Statistics, 35 (6), 2365-2369. http://dx.doi.org/10.1214/009053607000000442
Date Posted: 27 November 2017
This document has been peer reviewed.