Your copula is a classifier in disguise: classification-based copula density estimation

Autor:	Huk, David, Steel, Mark, Dutta, Ritabrata
Rok vydání:	2024
Předmět:	Statistics - Methodology Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We propose reinterpreting copula density estimation as a discriminative task. Under this novel estimation scheme, we train a classifier to distinguish samples from the joint density from those of the product of independent marginals, recovering the copula density in the process. We derive equivalences between well-known copula classes and classification problems naturally arising in our interpretation. Furthermore, we show our estimator achieves theoretical guarantees akin to maximum likelihood estimation. By identifying a connection with density ratio estimation, we benefit from the rich literature and models available for such problems. Empirically, we demonstrate the applicability of our approach by estimating copulas of real and high-dimensional datasets, outperforming competing copula estimators in density evaluation as well as sampling. Comment: Preprint
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.03014 Zobrazit plný text záznamu View this record from Arxiv