Optimally distinguishable distributions: a new approach to composite hypothesis testing with applications to the classical linear model

Authors:
Seyed Alireza Razavi;Ciprian Doru Giurcaneanu
Affiliations:
Department of Signal Processing, Tampere University of Technology, Tampere, Finland;Department of Signal Processing, Tampere University of Technology, Tampere, Finland
Venue:
IEEE Transactions on Signal Processing
Year:
2009

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Abstract

The newest approach to composite hypothesis testing proposed by Rissanen relies on the concept of optimally distinguishable distributions (ODD). The method is promising, but so far it has only been applied to a few simple examples.We derive the ODD detector for the classical linear model. In this framework, we provide answers to the following problems that have not been previously investigated in the literature: i) the relationship betweenODDand the widely used Generalized Likelihood Ratio Test (GLRT); ii) the connection between ODD and the information theoretic criteria applied in model selection. We point out the strengths and the weaknesses of the ODD method in detecting subspace signals in broadband noise. Effects of the subspace interference are also evaluated.