Afrika Statistika

Bootstrap Bartlett Adjustment on Decomposed Variance-Covariance Matrix of Seemingly Unrelated Regression Model

Oluwayemisi Oyeronke ALABA and Afeez Abolaji LAWAL

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We investigated hypothesis testing in Seemingly Unrelated Regression (SUR) using Log Likelihood Ratio (LLR) test. The asymptotic distribution of this statistic is well documented in literature to have substantial inaccuracy by an order of magnitude leading to the rejection of too many true null hypotheses. Bartlett adjustment of Barndorff and Blaesild and Efron's bootstrap methods were considered to provide more accurate significance level to the distribution. Simulation results from the partitioned variance-covariance matrix showed that the lower triangular matrix performed better than the upper triangular matrix. The Bartlett method of Barndorff and Blaesild provided better significance value than the bootstrap method.


Ici, nous étudions des tests d'hypothèses dans une regression avec vraisemblance de non-correlation, basée le rapport du logarithme de la vraisemblance. La distribution asymptotique de la statistique utilisée est connue pour avoir une grande efficacité. Pour rémédier à cette situation, deux types dùajustement sont considerés : un base sur la méthode de Bartlett et un autre base sur la méthode de Barndorff et Blaesid. Une étude de simulation montre l'éfficacité des méthodes d'ajustement et la superiorité du second ajustement sur le premier.

Article information

Afr. Stat., Volume 14, Number 1 (2019), 1891-1902.

First available in Project Euclid: 24 May 2019

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Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62J05: Linear regression 62F03: Hypothesis testing

Bartlett adjustment bootstrap generalised least squares likelihood ratio test maximum likelihood triangular matrices seemingly unrelated regression


ALABA, Oluwayemisi Oyeronke; LAWAL, Afeez Abolaji. Bootstrap Bartlett Adjustment on Decomposed Variance-Covariance Matrix of Seemingly Unrelated Regression Model. Afr. Stat. 14 (2019), no. 1, 1891--1902. doi:10.16929/as/2019.1891.140.

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