题 目: Robust Regression Shrinkage and Consistent Variable Selection via the LAD-Lasso

汇报人: 王汉生(副教授)

拉斯维加斯9888治理学院商务统计与经济计量系

时 间: 3月22日(周三)3:30-5:00pm

地 点: GSM115

Abstract: The least absolute deviation (LAD) regression is a useful method for robust regression while the least absolute shrinkage and selection

operator (lasso) is a popular choice for shrinkage estimation and

variable selection. In this article, we attempt to combine these two

classical ideas together to produce LAD-lasso. Compared with the LAD

regression, LAD-lasso can do parameter estimation and variable selection

simultaneously. Compared with the traditional lasso, LAD-lasso is resistant to heavy-tailed errors or outliers in the response. Furthermore, with easily estimated tuning parameters, the LAD-lasso estimator enjoys the same asymptotic efficiency as the unpenalized LAD estimator obtained under the {it true} model (i.e. the oracle property). Extensive simulation studies demonstrate the satisfactory finite sample performance of LAD-lasso and a real example is analyzed for illustration purpose.

For a complete PDF file:

http://hansheng.gsm.pku.edu.cn/pdf/2006/LAD-Lasso.pdf