A variational approach to a cumulative distribution function estimation problem under stochastic ambiguity

We propose a method for finding a cumulative distribution function (cdf) that minimizes the (regularized) distance to a given cdf, while belonging to an ambiguity set constructed relative to another cdf and, possibly, incorporating soft information. Our method embeds the family of cdfs onto the space of upper semicontinuous functions endowed with the hypo-distance. In this setting, we present an approximation scheme based on epi-splines, defined as piecewise polynomial functions, and use bounds for estimating the hypo-distance. Under appropriate hypotheses, we guarantee that the cluster points corresponding to the sequence of minimizers of the resulting approximating problems are solutions to a limiting problem. In addition, we describe a large class of functions that satisfy these hypotheses. The approximating method produces a linear-programming-based approximation scheme, enabling us to develop an algorithm from off-the-shelf solvers. The convergence of our proposed approximation is illustrated by numerical examples for the bivariate case, one of which entails a Lipschitz condition.