Alternative Procedures to Discriminate Non Nested Multivariate Linear Regression Models

This article builds classical and Bayesian testing procedures for choosing between non nested multivariate regression models. Although there are several classical tests for discriminating univariate regressions, only the Cox test is able to consistently handle the multivariate case. We then derive t...

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Bibliographic Details
Published in:Communications in statistics. Theory and methods 2005-10, Vol.34 (9-10), p.2047-2062
Main Authors: Araújo, Maria Ivanilde, Fernandes, Marcelo, Pereira, Basilio de Bragança
Format: Article
Language:English
Subjects:
Bayes factors: Intrinsic
Exact sciences and technology
Fractional
Linear inference, regression
Mathematics
Multivariate analysis
Non nested hypothesis
Posterior
Probability and statistics
Sciences and techniques of general use
Separate families of hypothesis
Statistics
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Summary:This article builds classical and Bayesian testing procedures for choosing between non nested multivariate regression models. Although there are several classical tests for discriminating univariate regressions, only the Cox test is able to consistently handle the multivariate case. We then derive the limiting distribution of the Cox statistic in such a context, correcting an earlier derivation in the literature. Further, we show how to build alternative Bayes factors for the testing of nonnested multivariate linear regression models. In particular, we compute expressions for the posterior Bayes factor, the fractional Bayes factor, and the intrinsic Bayes factor.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610920500203687