ROBUST LINEAR-REGRESSION IN REPLICATED MEASUREMENT ERROR MODELS

被引：10

作者：

CARROLL, RJ

ELTINGE, JL

RUPPERT, D

机构：

[1] TEXAS A&M UNIV SYST,DEPT STAT,COLL STN,TX 77843

[2] CORNELL UNIV,SCH OPERAT RES & IND ENGN,ITHACA,NY 14853

来源：

STATISTICS & PROBABILITY LETTERS | 1993年 / 16卷 / 03期

关键词：

ASYMPTOTIC THEORY; BOUNDED INFLUENCE ESTIMATORS; ERRORS IN VARIABLES; LEVERAGE; MALLOWS ESTIMATES;

D O I：

10.1016/0167-7152(93)90139-A

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 propose robust and bounded influenCe methods for linear regression when some of the predictors are measured with error. We address the important special CaSe that the surrogate predictors are replicated, and that the measurement errors in response and predictors are correlated. The robust methods proposed are variants of the so-called Mallows class of estimates. The resulting estimators are easily computed and have a simple asymptotic theory. An example is used to illustrate the results.

引用

页码：169 / 175

页数：7

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