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

被引：0
|
作者：
Miao Baiqi [1 ]
Wu Yuehua [2 ]
Liu Donghai [3 ]
机构：
[1] Univ Sci & Technol China, Dept Stat & Finance, Hefei 230026, Peoples R China
[2] York Univ, Dept Math & Stat, Toronto, ON M3J 2R7, Canada
[3] Chinese Peoples Armed Police Forces Acad, Dept Fire Command, Langfang 065000, Peoples R China
来源：
ACTA MATHEMATICA SCIENTIA | 2010年 / 30卷 / 01期
基金：
中国国家自然科学基金; 加拿大自然科学与工程研究理事会;
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; DEPENDENT SEQUENCES; LOCATION; SCATTER;
D O I：
暂无
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
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 u(1)(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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