A Robust Hybrid of Lasso and LARR

摘要：

Algorithms such as lasso and locally adjusted robust regressor (LARR) are of great interest because of their resulted sparse models for interpretation in addition to prediction. In this paper, we combine these two classical ideas together to produce LARR-lasso. Compared with the LARR regression, LARR-lasso can do parameter estimation and variable selection simultaneously. Compared with the traditional lasso, LARR-lasso is resistant to heavy-tailed errors or outliers in the response. Through the local linear approximation to the non-concave penalty functions, the problem of precision matrix estimation is recast as a sequence of penalized likelihood problems with a weighted L1 penalty. Our estimation schemes are applied to two real datasets. Simulation experiments and asymptotic theory are used to justify our proposed methods.

关键词： LARR-lasso LARR Lasso Regression Subset Selection

作者: CHENG Xiang TIAN Yuan WANG SuLi LI Bu Sheng

作者单位: Information engineering Institute Jingdezhen Ceramic Institute, Jingdezhen, P.R.China, 333000

会议类型: 国际会议

会议名称: 2009 International Institute of Applied Statistics Studies(2009 国际应用统计学术研讨会)

会议地点: 青岛

会议语种:英文

页码: 1-4

在线出版日期: 2009-07-25（万方平台首次上网日期，不代表论文的发表时间）