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Constrained test in linear models with multivariate power exponential distribution

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Abstract

We investigate the problem of testing equality and inequality constraints on regression coefficients in linear models with multivariate power exponential (MPE) distribution. This distribution has received considerable attention in recent years and provides a useful generalization of the multivariate normal distribution. We examine the performance of the power of the likelihood ratio, Wald and Score tests for grouped data and in the presence of regressors, in small and moderate sample sizes, using Monte Carlo simulations. Additionally, we present a real example to illustrate the performance of the proposed tests under the MPE model.

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Notes

  1. Note that the likelihood ratio, score and Wald tests are asymptotically distributed as a mixture of chi-square distributions with weights not depending on null parameters, but possibly depending on correlations. Therefore, in general regression models, the problem of finding the least favorable point in the null hypothesis translates into a problem of searching through a set of correlation coefficients for the least favorable points; see Cysneiros and Paula (2004).

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Acknowledgments

The authors wish to thank an Associate Editor and two anonymous referees for their constructive comments on an earlier version of this manuscript which resulted in this improved version. J. Leão would like to thank CAPES for the financial support. H. Saulo gratefully acknowledges CNPq and CAPES for the financial support.

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Correspondence to Jeremias Leão.

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Leão, J., Cysneiros, F., Saulo, H. et al. Constrained test in linear models with multivariate power exponential distribution. Comput Stat 31, 1569–1592 (2016). https://doi.org/10.1007/s00180-016-0650-x

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  • DOI: https://doi.org/10.1007/s00180-016-0650-x

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