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\(M\)-estimates of regression when the scale is unknown and the error distribution is possibly asymmetric: a minimax result. (English) Zbl 1066.62510

Summary: Huber (1964) found the minimax-variance M-estimate of location under the assumption that the scale parameter is known; Li and Zamar (1991) extended this result to the case when the scale is unknown. We consider the robust estimation of the regression coefficients (\(\beta_1\), ..., \(\beta_p\)) when the scale and intercept parameters are unknown. The minimax-variance estimates of (\(\beta_1\), ..., \(\beta_p\)) with respect to the trace of their asymptotic covariance matrix are derived. The maximum is taken over \(\varepsilon\)-contamination neighbourhoods of a central regression model with Gaussian errors (asymmetric contamination is allowed), and the minimum is taken over a large class of generalized M-estimates of regression of the Mallow type. The optimal choice of estimates for the nuisance parameters (scale and intercept) is also considered.

MSC:

62F10 Point estimation
62J05 Linear regression; mixed models
62F35 Robustness and adaptive procedures (parametric inference)
62C20 Minimax procedures in statistical decision theory
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References:

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