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Uniformly most powerful unbiased test for conditional independence in Gaussian graphical model

Author

Listed:
  • Koldanov, Petr
  • Koldanov, Alexander
  • Kalyagin, Valeriy
  • Pardalos, Panos

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 Neyman 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.

Suggested Citation

  • Koldanov, Petr & Koldanov, Alexander & Kalyagin, Valeriy & Pardalos, Panos, 2017. "Uniformly most powerful unbiased test for conditional independence in Gaussian graphical model," Statistics & Probability Letters, Elsevier, vol. 122(C), pages 90-95.
    • Handle: RePEc:eee:stapro:v:122:y:2017:i:c:p:90-95
    DOI: 10.1016/j.spl.2016.11.003
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    References listed on IDEAS

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    1. Mathias Drton, 2004. "Model selection for Gaussian concentration graphs," Biometrika, Biometrika Trust, vol. 91(3), pages 591-602, September.
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