S. Fortunati). ing (MRI), have highlighted the impulsive, heavy-tailed behaviour of the observations [1] . These experimental evidences have motivated the need to go beyond the Gaussian model and develop new statistical models able to better characterize the data. One of the more flexible and general non-Gaussian model is represented by the set of the Complex Elliptically Symmetric (CES) distributions [2] , also called Multivariate Elliptically Contoured distributions [3] . CES distributions encompasses the complex Gaussian, the Generalized Gaussian and all the Compound Gaussian (CG) distributions, such as the complex t -distribution and the K -distribution, as special cases. The pdf of a CES distributed N -dimensional random vector x l ∈ C N is completely characterized by the mean value γ, the scatter (or shape) matrix and by a real valued function w (t) : R + → R , called the density generator , i.e. x l ∼ CES N ( γ, , w ) [2,3] . The CES distributions have been used in a variety of applications, in particular in the radar and array signal processing fields.Other experimental evidences reveal recurring violations of the matched model assumption, that is the claim of a perfect match between the assumed and the true data model. The mathematical bases of a formal theory of the parameter estimation under model misspecification has been firstly developed by statisticians as Huber [4] , White [5] and Vuong [6] and recently rediscovered by the Signal Processing (SP) community [7-9] and applied to a va-
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