Robust regression estimation based on data partitioning

被引：0

作者：

Lee, Dong-Hee ^{[2
]}

Park, Yousung ^{[1
]}

机构：

[1] Korea Univ, Dept Stat, Seoul 136701, South Korea

[2] Korea Univ, BK21 Educ & Res Ctr Econ & Stat, Jochiwon Eup 339700, Chungnam, South Korea

来源：

JOURNAL OF THE KOREAN STATISTICAL SOCIETY | 2007年 / 36卷 / 02期

关键词：

computation problem; data partitioning; efficiency; high breakdown point; outlier detection; performance in large sample;

D O I：

暂无

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We introduce a high breakdown point estimator referred to as data partitioning robust regression estimator (DPR). Since the DPR is obtained by partitioning observations into a finite number of subsets, it has no computational problem unlike the previous robust regression estimators. Empirical and extensive simulation studies show that the DPR is superior to the previous robust estimators. This is much so in large samples.

引用

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页码：299 / 320

页数：22

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