VARIANCE ESTIMATION IN HIGH DIMENSIONAL REGRESSION MODELS
Links to Files
Permanent Link
Collections
Author/Creator
Author/Creator ORCID
Date
Type of Work
Department
Program
Citation of Original Publication
Chatterjee, Snigdhansu, and Arup Bose. “VARIANCE ESTIMATION IN HIGH DIMENSIONAL REGRESSION MODELS.” Statistica Sinica 10 (2000): 497–515.
Rights
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Subjects
Abstract
We treat the problem of variance estimation of the least squares estimate of the parameter in high dimensional linear regression models by using the Uncorrelated Weights Bootstrap (U BS). We ?nd a representation of the U BS dispersion matrix and show that the bootstrap estimator is consistent if p2/n ? 0 where p is the dimension of the parameter and n is the sample size. For ?xed dimension we show that the U BS belongs to the R-class as de?ned in Liu and Singh (1992).