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Selecting an adaptive sequence for computing recursive M-estimators in multivariate linear regression models

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Abstract

In this paper, the authors consider an adaptive recursive algorithm by selecting an adaptive sequence for computing M-estimators in multivariate linear regression models. Its asymptotic property is investigated. The recursive algorithm given by Miao and Wu (1996) is modified accordingly. Simulation studies of the algorithm is also provided. In addition, the Newton-Raphson iterative algorithm is considered for the purpose of comparison.

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Authors and Affiliations

  1. Department of Statistics and Finance, University of Science and Technology of China, Hefei, 230026, China

    Baiqi Miao & Baisuo Jin

  2. Department of Mathematics and Statistics, York University, Toronto, Ontario, M3J1P3, Canada

    Qian Tong & Yuehua Wu

Authors
  1. Baiqi Miao
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  2. Qian Tong
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  3. Yuehua Wu
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  4. Baisuo Jin
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Corresponding author

Correspondence to Baisuo Jin.

Additional information

This research is supported by the National Natural Science Foundation for Young Scientists of China under Grant No. 11101397 and the Natural Sciences and Engineering Research Council of Canada.

This paper was recommended for publication by Editor ZOU Guohua.

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Miao, B., Tong, Q., Wu, Y. et al. Selecting an adaptive sequence for computing recursive M-estimators in multivariate linear regression models. J Syst Sci Complex 26, 583–594 (2013). https://doi.org/10.1007/s11424-013-1113-x

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  • DOI: https://doi.org/10.1007/s11424-013-1113-x

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