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.
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页码：319 / 329
页数：11
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