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Neighborhoods as Nuisance Parameters? Robustness vs. Semiparametrics

By Helmut Rieder


Deviations from the center within a robust neighborhood may naturally be considered an innite dimensional nuisance parameter. Thus, the semiparametric method may be tried, which is to compute the scores function for the main parameter minus its orthogonal projection on the closed linear tangent space for the nuisance parameter, and then rescale for Fisher consistency. We derive such a semiparametric influence curve by nonlinear projection on the tangent balls arising in robust statistics. This semiparametric influence curve is then compared with the optimally robust influence curve that minimizes maximum weighted mean square error of the corresponding asymptotically linear estimators over infinitesimal neighborhoods. While there is coincidence for Hellinger balls, at least clipping is achieved for total variation and contamination neighborhoods, but the semiparametric method in general falls short to solve the minimax MSE estimation problem for the gross error models. The semiparametric..

Topics: and balls, projection, in uence curves
Year: 2000
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