Inference and Sampling for Archimax Copulas

TL;DR

This paper introduces a novel non-parametric inference and sampling method for Archimax copulas, enabling flexible, scalable modeling of multivariate dependencies in both the bulk and tails of distributions, with promising applications in safety-critical fields.

Contribution

It develops the first highly flexible and scalable inference and sampling algorithms for Archimax copulas, leveraging their stochastic representation.

Findings

Effective tail extrapolation demonstrated in experiments

Scales well to high-dimensional data

Outperforms existing density modeling techniques

Abstract

Understanding multivariate dependencies in both the bulk and the tails of a distribution is an important problem for many applications, such as ensuring algorithms are robust to observations that are infrequent but have devastating effects. Archimax copulas are a family of distributions endowed with a precise representation that allows simultaneous modeling of the bulk and the tails of a distribution. Rather than separating the two as is typically done in practice, incorporating additional information from the bulk may improve inference of the tails, where observations are limited. Building on the stochastic representation of Archimax copulas, we develop a non-parametric inference method and sampling algorithm. Our proposed methods, to the best of our knowledge, are the first that allow for highly flexible and scalable inference and sampling algorithms, enabling the increased use of Archimax copulas in practical settings. We experimentally compare to state-of-the-art density modeling techniques, and the results suggest that the proposed method effectively extrapolates to the tails while scaling to higher dimensional data. Our findings suggest that the proposed algorithms can be used in a variety of applications where understanding the interplay between the bulk and the tails of a distribution is necessary, such as healthcare and safety.