Adjusted likelihood inference in an elliptical multivariate errors-in-variables model

Details Adjusted likelihood inference in an elliptical multivariate errors-in-variables model Adjusted likelihood inference in an elliptical multivariate errors-in-variables model

2011-08-04

arxiv.org/abs/1108.1098v1

Mathematics

Statistics Theory

Scientific paper

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.

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