Summary
Testing that an edge can be excluded from a graphical Gaussian model is an important step in model fitting and the form of the generalised likelihood ratio test statistic for this hypothesis is well known. Herein the modified profile likelihood test statistic for this hypothesis is obtained in closed form and is shown to be a function of the sample partial correlation. Related expressions are given for the Wald and the efficient score statistics. Asymptotic expansions of the exact distribution of this correlation coefficient under the hypothesis of conditional independence are used to compare the adequacy of the chi-squared approximation of these and Fisher’s Z statistics. While no statistic is uniformly best approximated, it is found that the coefficient of the O(n-1) term is invariant to the dimension of the multivariate Normal distribution in the case of the modified profile likelihood and Fisher’s Z but not for the other statistics. This underlines the importance of adjusting test statistics when there are large numbers of variables, and so nuisance parameters in the model.
Similar comparisons are effected for the Normal approximation to the signed square-rooted versions of these statistics.
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Smith, P.W.F., Whittaker, J. (1998). Edge Exclusion Tests for Graphical Gaussian Models. In: Jordan, M.I. (eds) Learning in Graphical Models. NATO ASI Series, vol 89. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5014-9_21
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DOI: https://doi.org/10.1007/978-94-011-5014-9_21
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