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

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 regres-sion 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.

作 者： Miao Baiqi Wu Yuehua Liu Donghai   作者單位： Miao Baiqi(Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026, China)

Wu Yuehua(Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada)

Liu Donghai(Department of Fire Command, Chinese People's Armed Police Forces Academy, Langfang 065000, China) 

刊 名： 數(shù)學物理學報（英文版）  ISTIC SCI 英文刊名： ACTA MATHEMATICA SCIENTIA  年，卷(期)： 2010 30(1)  分類號： O1  關鍵詞： asymptotic efficiency   asymptotic normality   asymptotic relative efficiency   least absolute deviation   least squares   M-estimation   multivariate linear   optimal estimator   recursive algorithm   regression coefficients   robust esti-mation   regression model  

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