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Optimal Multiple Decision Statistical Procedure for Inverse Covariance Matrix

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Constructive Nonsmooth Analysis and Related Topics

Part of the book series: Springer Optimization and Its Applications ((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.

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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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Authors and Affiliations

  1. National Research University, Higher School of Economics, Bolshaya Pecherskaya 25/12, Nizhny Novgorod, 603155, Russia

    Alexander P. Koldanov & Petr A. Koldanov

Authors
  1. Alexander P. Koldanov
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  2. Petr A. Koldanov
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Corresponding author

Correspondence to Petr A. Koldanov .

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Editors and Affiliations

  1. Saint Petersburg State University, Saint Petersburg, Russia

    Vladimir F. Demyanov

  2. Department of Industrial & Systems Engin, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  3. Higher School of Economics, National Research University, Nizhny Novgorod, Russia

    Mikhail Batsyn

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Koldanov, A.P., Koldanov, P.A. (2014). Optimal Multiple Decision Statistical Procedure for Inverse Covariance Matrix. In: Demyanov, V., Pardalos, P., Batsyn, M. (eds) Constructive Nonsmooth Analysis and Related Topics. Springer Optimization and Its Applications, vol 87. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8615-2_13

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