Robust Hypothesis Testing With a Relative Entropy Tolerance

Robust Hypothesis Testing With a Relative Entropy Tolerance

This paper considers the design of a minimax test for two hypotheses where the actual probability densities of the observations are located in neighborhoods obtained by placing a bound on the relative entropy between actual and nominal densities. The minimax problem admits a saddle point which is ch...

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Veröffentlicht in:IEEE transactions on information theory 2009-01, Vol.55 (1), p.413-421
1. Verfasser: Levy, B.C.
Format: Artikel
Sprache:eng
Schlagworte:
Additive noise
Applied sciences
Context modeling
Data transmission
Degradation
Density
Detectors
Entropy
Exact sciences and technology
Flattening
Hypothesis testing
Information theory
Information, signal and communications theory
Kullback-Leibler divergence
least favorable densities
Likelihood ratio
min-max problem
Minimax technique
Minimax techniques
Noise
Noise robustness
Pollution measurement
robust hypothesis testing
saddle point
Saddle points
Signal detection
Telecommunications and information theory
Testing
Tolerances
Transformations
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Beschreibung
Zusammenfassung:This paper considers the design of a minimax test for two hypotheses where the actual probability densities of the observations are located in neighborhoods obtained by placing a bound on the relative entropy between actual and nominal densities. The minimax problem admits a saddle point which is characterized. The robust test applies a nonlinear transformation which flattens the nominal likelihood ratio in the vicinity of one. Results are illustrated by considering the transmission of binary data in the presence of additive noise.
ISSN:0018-9448
1557-9654
DOI:10.1109/TIT.2008.2008128