LIMITING BEHAVIOR OF RECURSIVE M-ESTIMATORS IN MULTIVARIATE LINEAR REGRESSION MODELS AND THEIR ASYMPTOTIC EFFICIENCIES

被引：0

作者：

缪柏其 ^{[1
]}

吴月华 ^{[2
]}

刘东海 ^{[3
]}

机构：

[1] Department of Statistics and Finance,University of Science and Technology of China

[2] Department of Mathematics and Statistics,York University,Toronto,Ontario,Canada

[3] Department of Fire Command,Chinese People's Armed Police Forces Academy

来源：

Acta Mathematica Scientia | 2010年 / 30卷 / 01期

基金：

加拿大自然科学与工程研究理事会; 中国国家自然科学基金;

关键词：

asymptotic efficiency; asymptotic normality; asymptotic relative efficiency; least absolute deviation; least squares; M-estimation; multivariate linear; optimal estimator; recursive algorithm; regression coefficients; robust estimation; regression model;

D O I：

暂无

中图分类号：

O212 [数理统计];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Recursive algorithms are very useful for computing M-estimators of regression coefficients and scatter parameters.In this article,it is shown that for a nondecreasing u1（t）,under some mild conditions the recursive M-estimators of regression coefficients and scatter parameters are strongly consistent and the recursive M-estimator of the regression coefficients is also asymptotically normal distributed.Furthermore,optimal recursive M-estimators,asymptotic efficiencies of recursive M-estimators and asymptotic relative efficiencies between recursive M-estimators of regression coefficients are studied.

引用

页码：319 / 329

页数：11

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