The Asymptotics of the Mahalanobis Depth-Based Theil-Sen Estimators in Multiple Linear Models

نویسندگان

  • Fang Li
  • Hanxiang Peng
  • Fei Tan
چکیده

In this article, we propose the use of the Mahalanobis depth to construct fully affine equivalent Theil-Sen estimators of parameters in a multiple linear model. Our construction includes the spatial depth-based Theil-Sen estimators as a special case. We exhibit that the efficiency of the proposed estimators relative to the least squares estimators increases with the number of parameters under independent and identically distributed normal errors. We point out that the estimators are robust with a possible maximal breakdown point and possess bounded influences. We show that the estimators are consistent and asymptotic normal for both deterministic and random covariates under suitable conditions. AMS 2000 subject classification: primary 62G05; 62G20.

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تاریخ انتشار 2009