Using mixtures in seemingly unrelated linear regression models with non-normal errors
Methodology
2014-03-18 v1
Abstract
Seemingly unrelated linear regression models are introduced in which the distribution of the errors is a finite mixture of Gaussian components. Identifiability conditions are provided. The score vector and the Hessian matrix are derived. Parameter estimation is performed using the maximum likelihood method and an Expectation-Maximisation algorithm is developed. The usefulness of the proposed methods and a numerical evaluation of their properties are illustrated through the analysis of a real dataset.
Cite
@article{arxiv.1403.4135,
title = {Using mixtures in seemingly unrelated linear regression models with non-normal errors},
author = {Giuliano Galimberti and Elena Scardovi and Gabriele Soffritti},
journal= {arXiv preprint arXiv:1403.4135},
year = {2014}
}