The authors consider the robust detection of a known constant signal in additive, mutually independent and identically distributed noise which is nominally Laplace. Employing the classical saddlepoint methodology of Huber (1951), they specify the form of the robust detector and obtain closed form results which allow for the analysis of the performance of the robust detector. They show via examples that the robustness can entail a considerable cost in detector performance. In addition, they compare the performance of the robust detector to that of the Neyman-Pearson detector for examples where the noise is Laplace and also where the noise is least favorable for the given detector.>

A new approach toward robust parameter estimation is presented. As opposed to schemes based on classical saddlepoint techniques, the methods apply quite naturally to dependent and/or nonstationary data and also have the advantage of providing a quantitative measure of the degree of robustness offered by a particular estimator. It is shown why censoring observations of large magnitude does indeed impart robustness, and it also shown how nontraditional schemes have certain advantages, particularly when trading off performance and robustness. The application of the results is further demonstrated by including several illustrative examples.>