Fast robust variable selection

被引：0

作者：

Van Aelst, Stefan ^{[1
]}

Khan, Jafar A. ^{[2
]}

Zamar, Ruben H. ^{[3
]}

机构：

[1] Univ Ghent, Dept Appl Math & Comp Sci, B-9000 Ghent, Belgium

[2] Univ Dhaka, Dept Stat, Dhaka, Bangladesh

[3] Univ British Columbia, Dept Stat, Vancouver, BC V5Z 1M9, Canada

来源：

COMPSTAT 2008: PROCEEDINGS IN COMPUTATIONAL STATISTICS | 2008年

关键词：

correlation; missing data; robustness; variable selection;

D O I：

暂无

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

We discuss some computationally efficient procedures for robust variable selection in linear regression. A key component in these procedures is the computation of robust correlations between pairs of variables. We show that the robust variable selection procedures can easily handle missing data under the assumption that data are missing completely at random.

引用

页码：359 / +

页数：3

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