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Qualitative robustness is of little help in the actual selection of a robust procedure suited for a particular application. In order to make a rational choice, we must introduce quantitative aspects as well. Anscombe s (1960) comparison of the situation with an insurance problem is very helpful. Typically, a so-called classical procedure is the optimal procedure for some ideal (usually normal) model. If it happens to be nonrobust and we want to insure against accidents caused by deviations from the model, we clearly will have to pay for it by sacrificing some efficiency at the model. The questions are, of course, how much efficiency we are willing to sacrifice, and against how bad a deviation we would like to insure. One possible approach is to fix a certain neighborhood of the model and to safeguard within that neighborhood (Huber 1964). In the simple location case, this leads to quite manageable minimax problems (even though the space of pure strategies for Nature is not dominated), both for asymptotic performance criteria (asymptotic

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The projmod command modifies the project s information on the local system. In this example, we are adding a description for the project:

REPLACING THE GAUSSIAN BY A VOIGTIAN representing the convolution gives: V (Ei ) =

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If we choose 6 sufficiently small, this is of a smaller order of magnitude than E . Compare also Kersting (1978). On the other hand (2.35) is true for dp and d g L if F is the uniform distribution on a finite interval, but it fails again for the uniform distribution on the unit cube in three or more dimensions [see Dudley (1969) for details]. It seems that we are in trouble here because of a phenomenon that has been colorfully called the curse of dimensionality ; the higher the dimension, the more empty space there is, and it becomes progressively more difficult to relate the coarse and sparse empirical measure to the true one. Mere Gsteaux differentiability does not suffice to establish asymptotic normality [unless we also have higher order derivatives, cf. von Mises (1937, 1947), who introduced the concept of differentiable statistical functionals, and Filippova (1962)l. The most promising intermediate approach seems to be that of Reeds (1976), which is based on the notion of compact differentiability (Averbukh and Smolyanov 1967, 1968). See also Clarke (1983, 1986). In any case, an approach through differentiable functionals does not seem to be adequate for dealing with non-smooth true underlying distributions and for establishing finer details, such as the non-normal limiting distributions occurring in Section 3.2.2. See also Sections 6.2 and 6.3.

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