The trivariate wrapped Cauchy copula

Fuente: arXiv
Enregistré dans:
Détails bibliographiques
Auteurs principaux: Kato, Shogo, Ley, Christophe, Loizidou, Sophia, Mardia, Kanti V.
Format: Preprint
Publié: 2024
Sujets:
Accès en ligne:
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
_version_ 1866912684151144448
author Kato, Shogo
Ley, Christophe
Loizidou, Sophia
Mardia, Kanti V.
author_facet Kato, Shogo
Ley, Christophe
Loizidou, Sophia
Mardia, Kanti V.
contents In this paper, we propose a new flexible distribution for data on the three-dimensional torus which we call a trivariate wrapped Cauchy copula. Our trivariate copula has several attractive properties. It has a simple form of density and desirable modality properties. Its parameters allow for an adjustable degree of dependence between every pair of variables and these can be easily estimated. The conditional distributions of the model are well studied bivariate wrapped Cauchy distributions. Furthermore, the distribution can be easily simulated. Parameter estimation via maximum likelihood for the distribution is given and we highlight the simple implementation procedure to obtain these estimates. We illustrate our trivariate wrapped Cauchy copula on data from protein bioinformatics of conformational angles.
format Preprint
id arxiv_https___arxiv_org_abs_2401_10824
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle The trivariate wrapped Cauchy copula
Kato, Shogo
Ley, Christophe
Loizidou, Sophia
Mardia, Kanti V.
Methodology
62H11
In this paper, we propose a new flexible distribution for data on the three-dimensional torus which we call a trivariate wrapped Cauchy copula. Our trivariate copula has several attractive properties. It has a simple form of density and desirable modality properties. Its parameters allow for an adjustable degree of dependence between every pair of variables and these can be easily estimated. The conditional distributions of the model are well studied bivariate wrapped Cauchy distributions. Furthermore, the distribution can be easily simulated. Parameter estimation via maximum likelihood for the distribution is given and we highlight the simple implementation procedure to obtain these estimates. We illustrate our trivariate wrapped Cauchy copula on data from protein bioinformatics of conformational angles.
title The trivariate wrapped Cauchy copula
topic Methodology
62H11
url https://arxiv.org/abs/2401.10824