Adjusted likelihood inference in an elliptical multivariate errors-in-variables model
Abstract
In this paper we obtain an adjusted version of the likelihood ratio test for errors-in-variables multivariate linear regression models. The error terms are allowed to follow a multivariate distribution in the class of the elliptical distributions, which has the multivariate normal distribution as a special case. We derive a modified likelihood ratio statistic that follows a chi-squared distribution with a high degree of accuracy. Our results generalize those in Melo and Ferrari(Advances in Statistical Analysis, 2010, 94, 75-87) by allowing the parameter of interest to be vector-valued in the multivariate errors-in-variables model. We report a simulation study which shows that the proposed test displays superior finite sample behavior relative to the standard likelihood ratio test.
Cite
@article{arxiv.1108.1098,
title = {Adjusted likelihood inference in an elliptical multivariate errors-in-variables model},
author = {Tatiane F. N. Melo and Silvia L. P. Ferrari},
journal= {arXiv preprint arXiv:1108.1098},
year = {2011}
}