JSM 2012 Home

JSM 2012 Online Program

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

Online Program Home

Abstract Details

Activity Number: 613
Type: Contributed
Date/Time: Thursday, August 2, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #305449
Title: Bootstrap Methods for Lasso-Type Regression Estimators
Author(s): Wenlong Cai*+ and Stephen Man Sing Lee
Companies: The University of Hong Kong and The University of Hong Kong
Address: 518 Meng Wah Complex,, Hong Kong, , Hong Kong
Keywords: bootstrap ; lasso-type estimator ; estimation of distribution ; asymptotic distribution ; uniform consistency ; Oracle property

We study the distributions of Lasso-type regression estimators in a moving-parameter asymptotic framework, and consider various bootstrap methods for estimating them accordingly. We show, in particular, that the distribution functions of Lasso-type estimators, including even those possessing the oracle properties such as the adaptive Lasso and the SCAD, cannot be consistently estimated by the bootstraps uniformly over the space of the regression parameters, especially when some of the regression coefficients lie close to the origin. Such lack of uniform consistency poses difficulties in practical applications of the bootstraps for making Lasso-based inferences. In the light of this seemingly negative result, we seek, however, to develop criteria for assessing the relative risks, phrased in terms of their uniform consistency properties, of the various bootstrap methods, based on which an optimal bootstrap strategy may be formulated in an adaptive manner. A simulation study is provided to demonstrate the non-normal nature of the distributions of Lasso-type estimators, and to assess the performances of various bootstrap estimates across different values of regression parameters.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2012 program

2012 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.