Robust Semiparametric Graphical Models with Skew-Elliptical Distributions

Fuente: arXiv
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Main Authors: Di Luzio, Gabriele, Morelli, Giacomo
Format: Preprint
Published: 2025
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_version_ 1866914176503382016
author Di Luzio, Gabriele
Morelli, Giacomo
author_facet Di Luzio, Gabriele
Morelli, Giacomo
contents We propose semiparametric estimators, called elliptical skew-(S)KEPTIC, for efficiently and robustly estimating non-Gaussian graphical models. Our approach extends the semiparametric elliptical framework to the meta skew-elliptical family, which accommodates skewness. Theoretically, we show that the elliptical skew-(S)KEPTIC estimators achieve robust convergence rates for both graph recovery and parameter estimation. Through numerical simulations, we illustrate the reliable graph recovery performance of the elliptical skew-(S)KEPTIC estimators. Finally, we apply the new method to the daily log-returns of the stocks in the S\&P 500 index and obtain a sparser graph than with Gaussian copula graphical models.
format Preprint
id arxiv_https___arxiv_org_abs_2501_08033
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Robust Semiparametric Graphical Models with Skew-Elliptical Distributions
Di Luzio, Gabriele
Morelli, Giacomo
Methodology
We propose semiparametric estimators, called elliptical skew-(S)KEPTIC, for efficiently and robustly estimating non-Gaussian graphical models. Our approach extends the semiparametric elliptical framework to the meta skew-elliptical family, which accommodates skewness. Theoretically, we show that the elliptical skew-(S)KEPTIC estimators achieve robust convergence rates for both graph recovery and parameter estimation. Through numerical simulations, we illustrate the reliable graph recovery performance of the elliptical skew-(S)KEPTIC estimators. Finally, we apply the new method to the daily log-returns of the stocks in the S\&P 500 index and obtain a sparser graph than with Gaussian copula graphical models.
title Robust Semiparametric Graphical Models with Skew-Elliptical Distributions
topic Methodology
url https://arxiv.org/abs/2501.08033