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!Canceled! Copula estimation via machine learning

Abstract

We construct a flexible, consistent, piecewise linear estimator for a copula, leveraging the patchwork copula formalization and various piecewise constant density estimators. While the patchwork structure imposes the grid, our estimator is data-driven and constructs the grid recursively from the data, minimizing a chosen distance on the copula space. Furthermore, while the addition of the copula constraints makes the available solutions for density estimation unusable, our estimator is only concerned with dependence and guarantees the uniformity of margins. Refinements such as localised dimension reduction and bagging are developed, analyzed, and tested through applications on simulated data.

Date
May 11, 2020 12:00 AM
Location
Lyon, France
Oskar Laverny
Oskar Laverny
Actuary - P.h.d

My research interests include dependences structures in high dimensions, copulas, code and actuarial sciences.

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