数学物理学报(英文版) ›› 2010, Vol. 30 ›› Issue (1): 319-329.doi: 10.1016/S0252-9602(10)60048-3

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

  

  1. Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026, China
  • 收稿日期:2006-10-13 修回日期:2007-12-10 出版日期:2010-01-20 发布日期:2010-01-20
  • 基金资助:

    The research of B. Q. Miao was partially supported by the Natural Sciences and Engineering Research Council of Canada, the National Natural Science Foundation of China and the Doctorial Fund of Education Ministry of China. The research of Y. Wu was partially supported by the Natural Sciences and Engineering Research Council of Canada. The research of Donghai Liu was partially supported by the National Natural Science Foundation of China.

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

  1. Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026, China
  • Received:2006-10-13 Revised:2007-12-10 Online:2010-01-20 Published:2010-01-20
  • Supported by:

    The research of B. Q. Miao was partially supported by the Natural Sciences and Engineering Research Council of Canada, the National Natural Science Foundation of China and the Doctorial Fund of Education Ministry of China. The research of Y. Wu was partially supported by the Natural Sciences and Engineering Research Council of Canada. The research of Donghai Liu was partially supported by the National Natural Science Foundation of China.

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.

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 stimation, regression model

Abstract:

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.

Key words: asymptotic efficiency, asymptotic normality,  asymptotic relative efficiency, least absolute deviation, least squares, M-estimation, multivariate linear, optimal estimator, recursive algorithm, regression coefficients, robust stimation, regression model

中图分类号: 

  • 62F10