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

DOI10.1016/j.spl.2016.11.003zbMath1463.62170arXiv1610.00316OpenAlexW2529527163MaRDI QIDQ504465

Panos M. Pardalos, Petr A. Koldanov, Valeriy A. Kalyagin, Alexander P. Koldanov

Publication date: 16 January 2017

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1610.00316

zbMATH Keywords

exponential familiesconditional independencemultivariate normal distributionsample partial correlation testtests of Neyman structureuniformly most powerful unbiased tests

Mathematics Subject Classification ID

Hypothesis testing in multivariate analysis (62H15) Probabilistic graphical models (62H22)

Related Items (2)

Unnamed Item ⋮ Loss function, unbiasedness, and optimality of Gaussian graphical model selection

Uses Software

• MIM

Cites Work

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• Multiple testing and error control in Gaussian graphical model selection
• A SINful approach to Gaussian graphical model selection
• Comparative Analysis of Two Similarity Measures for the Market Graph Construction
• A Theory of some Multiple Decision Problems, I
• Model selection for Gaussian concentration graphs
• Introduction to Graphical Modelling
• Testing Statistical Hypotheses

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