Robust estimation in multiple linear regression model with non-Gaussian noise

被引：30

作者：

Akkaya, Aysen D. ^{[1
]}

Tiku, Moti L. ^{[1
]}

机构：

[1] Middle E Tech Univ, Dept Stat, TR-06531 Ankara, Turkey

来源：

AUTOMATICA | 2008年 / 44卷 / 02期

关键词：

linear regression; robustness; data anomaly; modified maximum likelihood; outliers;

D O I：

10.1016/j.automatica.2007.06.029

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The traditional least squares estimators used in multiple linear regression model are very sensitive to design anomalies. To rectify the situation we propose a reparametrization of the model. We derive modified maximum likelihood estimators and show that they are robust and considerably more efficient than the least squares estimators besides being insensitive to moderate design anomalies. (C) 2007 Elsevier Ltd. All rights reserved.

引用

页码：407 / 417

页数：11

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