Optimally Distinguishable Distributions: a New Approach to Composite Hypothesis Testing With Applications to the Classical Linear Model

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 f...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2009-07, Vol.57 (7), p.2445-2455
Main Authors: Razavi, S.A., Giurcaneanu, C.D.
Format: Article
Language:English
Subjects:
Acoustic signal processing
Applied sciences
Detectors
Exact sciences and technology
Generalized likelihood ratio test
Inference algorithms
information theoretic criteria
Information theory
Information, signal and communications theory
Interference
linear model
Maximum likelihood detection
Maximum likelihood estimation
minimum description length
Miscellaneous
optimally distinguishable distributions
Signal detection
Signal processing
Signal processing algorithms
Telecommunications and information theory
Testing
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Summary: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 between ODD and 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.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2009.2017568