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We develop and analyze stochastic optimization algorithms for problems in which the ex-pected loss is strongly convex, and the optimum is (approximately)sparse. Previous approaches are able to exploit...
The performance of known and new parametric estimators for Archimedean copulas is investigated, with special focus on large dimensions. In particular,method-of-moments-like estimators based on pairwis...
In this paper we introduce an influence measure based on second order expansion of the RV and GCD measures for the comparison between unperturbed and perturbed eigenvectors of a symmetric matrix estim...

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