Learning algorithms for implicit generative models can optimize a variety of criteria that measure how the data distribution differs from the implicit model distribution, including the Wasserstein distance, the Energy distance, and the Maximum Mean Discrepancy criterion. A careful look at the geometries induced by these distances on the space of probability measures reveals interesting differen...

We propose a parametric version of Independent Component Analysis (ICA) via Copulas families of multivariate distributions that join univariate margins to multivariate distributions. Our procedure exploits the role for copula models in information theory and in measures of association, specifically: the use of copulae densities as parametric mutual information, and as measures of association on...

Estimation of small area means in the presence of area level auxiliary information is considered. A class of estimators based on local polynomial regression is proposed. The assumptions on the area level regression are considerably weaker than standard small area models. Both the small area mean functions and the between area variance function are modeled as smooth functions of area level covar...

Many data mining applications require a ranking, rather than a mere classification, of cases. Examples of these applications are widespread, including Internet search engines (ranking of pages returned) and customer relationship management(ranking of profitable customers). However, little theoretical foundation and practical guideline have been established to assess the merits of different rank...