Asymptotics for Lasso-type estimators
成果类型：
Article
署名作者：
Knight, K; Fu, WJ
署名单位：
University of Toronto; Michigan State University
刊物名称：
ANNALS OF STATISTICS
ISSN/ISSBN：
0090-5364
发表日期：
2000
页码：
1356-1378
关键词：
摘要：
We consider the asymptotic behavior of regression estimators that minimize the residual sum of squares plus a penalty proportional to Sigma\beta (j)\(gamma) for some gamma > 0. These estimators include the Lasso as a special case when gamma = 1. Under appropriate conditions, we show that the limiting distributions can have positive probability mass at 0 when the true value of the parameter is 0. We also consider asymptotics for nearly singular designs.