COVARIANCE ASSISTED SCREENING AND ESTIMATION.

ABSTRACT: Consider a linear model Y = X β + z, where X = X_n,p and z ~ N(0, I_n ). The vector β is unknown and it is of interest to separate its nonzero coordinates from the zero ones (i.e., variable selection). Motivated by examples in long-memory time series (Fan and Yao, 2003) and the change-point problem (Bhattacharya, 1994), we are primarily interested in the case where the Gram matrix G = X'X is non-sparse but sparsifiable by a finite order linear filter. We focus on the regime where signals are both rare and weak so that successful variable selection is very challenging but is still possible. We approach this problem by a new procedure called the Covariance Assisted Screeni

SUBMITTER: Ke BT 

PROVIDER: S-EPMC4274608 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature