On Optimization over Tail Distributions

TL;DR

This paper develops novel optimization techniques for tail distribution bounds, addressing issues of non-convergence and model misspecification in extreme event analysis, especially for heavy tails.

Contribution

It introduces a method transforming shape-constrained problems into moment problems and reformulates infinite support problems into compact support ones with slack variables.

Findings

Transforms shape constraints into moment problems.

Reformulates infinite support problems to compact support.

Addresses non-convergence issues in tail distribution optimization.

Abstract

We investigate the use of optimization to compute bounds for extremal performance measures. This approach takes a non-parametric viewpoint that aims to alleviate the issue of model misspecification possibly encountered by conventional methods in extreme event analysis. We make two contributions towards solving these formulations, paying especial attention to the arising tail issues. First, we provide a technique in parallel to Choquet's theory, via a combination of integration by parts and change of measures, to transform shape constrained problems (e.g., monotonicity of derivatives) into families of moment problems. Second, we show how a moment problem cast over infinite support can be reformulated into a problem over compact support with an additional slack variable. In the context of optimization over tail distributions, the latter helps resolve the issue of non-convergence of solutions when using algorithms such as generalized linear programming. We further demonstrate the applicability of this result to problems with infinite-value constraints, which can arise in modeling heavy tails.