Optimal Multiple Decision Statistical Procedure for Inverse Covariance Matrix

Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 87)

Abstract

A multiple decision statistical problem for the elements of inverse covariance matrix is considered. Associated optimal unbiased multiple decision statistical procedure is given. This procedure is constructed using the Lehmann theory of multiple decision statistical procedures and the conditional tests of the Neyman structure. The equations for thresholds calculation for the tests of the Neyman structure are analyzed.

Keywords

Inverse covariance matrix Tests of the Neyman structure Multiple decision statistical procedure Generating hypothesis 

Notes

Acknowledgements

The authors are partly supported by National Research University, Higher School of Economics, Russian Federation Government grant, N. 11.G34.31.0057

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.National Research University, Higher School of EconomicsNizhny NovgorodRussia

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