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Loss function, unbiasedness, and optimality of Gaussian graphical model selection
A Gaussian graphical model is a graphical representation of the dependence structure for
a Gaussian random vector. Gaussian graphical model selection is a statistical problem that
identifies the Gaussian graphical model from observations. There are several statistical
approaches for Gaussian graphical model identification. Their properties, such as unbiasedeness
and optimality, are not established. In this paper we study these properties.
We consider the graphical model selection problem in the framework of multiple decision
theory and suggest assessing these procedures using an additive loss function. Associated
risk function in this case is a linear combination of the expected numbers of the two types
of error (False Positive and False Negative). We combine the tests of a Neyman structure for
individual hypotheses with simultaneous inference and prove that the obtained multiple
decision procedure is optimal in the class of unbiased multiple decision procedures.