Uniformly most powerful unbiased test for conditional independence in Gaussian graphical model
Statistics Theory
2016-10-04 v1 Statistics Theory
Abstract
Model selection for Gaussian concentration graph is based on multiple testing of pairwise conditional independence. In practical applications partial correlation tests are widely used. However it is not known whether partial correlation test is uniformly most powerful for pairwise conditional independence testing. This question is answered in the paper. Uniformly most powerful unbiased test of Neymann structure is obtained. It turns out, that this test can be reduced to usual partial correlation test. It implies that partial correlation test is uniformly most powerful unbiased one.
Cite
@article{arxiv.1610.00316,
title = {Uniformly most powerful unbiased test for conditional independence in Gaussian graphical model},
author = {Koldanov Petr and Koldanov Alexander and Kalyagin Valeriy and Pardalos Panos},
journal= {arXiv preprint arXiv:1610.00316},
year = {2016}
}
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11 pages