报告人: 王汉生(副教授)
光华管理学院商务统计与经济计量系
时 间: 3月15日(周三)3:30-5:00pm
地 点: 北京大学光华管理学院115室
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
