Modified likelihood ratio tests in heteroskedastic multivariate regression models with measurement error

In this paper, we develop modified versions of the likelihood ratio test for multivariate heteroskedastic errors-in-variables regression models. The error terms are allowed to follow a multivariate distribution in the elliptical class of distributions, which has the normal distribution as a special...

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Bibliographic Details
Published in:Journal of statistical computation and simulation 2014-10, Vol.84 (10), p.2233-2247
Main Authors: Melo, Tatiane F.N., Ferrari, Silvia L.P., Patriota, Alexandre G.
Format: Article
Language:English
Subjects:
Accuracy
Cardiovascular disease
Computer simulation
elliptical distribution
errors-in-variables model
Likelihood ratio
Mathematical analysis
Mathematical models
measurement error
modified likelihood ratio statistic
multivariate regression model
Normal distribution
Permissible error
Regression
Simulation
Statistical tests
Statistics
Variables
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Summary:In this paper, we develop modified versions of the likelihood ratio test for multivariate heteroskedastic errors-in-variables regression models. The error terms are allowed to follow a multivariate distribution in the elliptical class of distributions, which has the normal distribution as a special case. We derive the Skovgaard-adjusted likelihood ratio statistics, which follow a chi-squared distribution with a high degree of accuracy. We conduct a simulation study and show that the proposed tests display superior finite sample behaviour as compared to the standard likelihood ratio test. We illustrate the usefulness of our results in applied settings using a data set from the WHO MONICA Project on cardiovascular disease.
ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2013.787691