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2010

Nonparametric detection of signals by information theoretic criteria: performance analysis and an improved estimator

8 years 6 months ago
Nonparametric detection of signals by information theoretic criteria: performance analysis and an improved estimator
Determining the number of sources from observed dataisafundamentalprobleminmanyscientificfields.Inthispaper we consider the nonparametric setting, and focus on the detection performance of two popular estimators based on information theoretic criteria, the Akaike information criterion (AIC) and minimum description length (MDL). We present three contributions on this subject. First, we derive a new expression for the detection performance of the MDL estimator, which exhibits a much closer fit to simulations in comparison to previous formulas. Second, we presentarandommatrixtheoryviewpointoftheperformanceofthe AIC estimator, including approximate analytical formulas for its overestimation probability. Finally, we show that a small increase in the penalty term of AIC leads to an estimator with a very good detectionperformanceandanegligible overestimationprobability.
Boaz Nadler
Added 22 May 2011
Updated 22 May 2011
Type Journal
Year 2010
Where TSP
Authors Boaz Nadler
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