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Bibliographic Details
Main Authors: Krupskii, Pavel, Nasri, Bouchra R, Remillard, Bruno N
Format: Preprint
Published: 2023
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Online Access:https://arxiv.org/abs/2305.02789
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author Krupskii, Pavel
Nasri, Bouchra R
Remillard, Bruno N
author_facet Krupskii, Pavel
Nasri, Bouchra R
Remillard, Bruno N
contents In this article, a copula-based method for mixed regression models is proposed, where the conditional distribution of the response variable, given covariates, is modelled by a parametric family of continuous or discrete distributions, and the effect of a common latent variable pertaining to a cluster is modelled with a factor copula. We show how to estimate the parameters of the copula and the parameters of the margins, and we find the asymptotic behaviour of the estimation errors. Numerical experiments are performed to assess the precision of the estimators for finite samples. An example of an application is given using COVID-19 vaccination hesitancy from several countries. Computations are based on R package CopulaGAMM.
format Preprint
id arxiv_https___arxiv_org_abs_2305_02789
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle On factor copula-based mixed regression models
Krupskii, Pavel
Nasri, Bouchra R
Remillard, Bruno N
Methodology
In this article, a copula-based method for mixed regression models is proposed, where the conditional distribution of the response variable, given covariates, is modelled by a parametric family of continuous or discrete distributions, and the effect of a common latent variable pertaining to a cluster is modelled with a factor copula. We show how to estimate the parameters of the copula and the parameters of the margins, and we find the asymptotic behaviour of the estimation errors. Numerical experiments are performed to assess the precision of the estimators for finite samples. An example of an application is given using COVID-19 vaccination hesitancy from several countries. Computations are based on R package CopulaGAMM.
title On factor copula-based mixed regression models
topic Methodology
url https://arxiv.org/abs/2305.02789