Abstract
Many assumptions commonly made in science and engineering problems are at most approximations to reality and they do not always hold unfortunately. Recognizing this fact, the concept of robustness has been attracted and robust procedures have been developed to cover this fact. One of the most important ideas on this direction is Huber's M-estimator (maximum likelihood type estimator). Identifying the neighbourhoods of stochastic models in terms of the class of ε-contaminated probability distribution, he derived an estimator that minimizes the maximum degradation of performance possible for an ॉ-deviation from the assumption. This idea, however, is not applicable in practice since the exact value of the gross error ε is not known. In this paper, an M-estimator applicable in such situations is derived. A numerical example is presented for illustrating the proposed idea.
Published Version
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