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

Authors:
Boaz Nadler
Affiliations:
Department of Computer Science and Applied Mathematics Weizmann Institute of Science, Rehovot, Israel
Venue:
IEEE Transactions on Signal Processing
Year:
2010

Citing 12
Cited 2

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Detection of signals by information theoretic criteria: generalasymptotic performance analysis

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Source Enumeration for High-Resolution Array Processing Using Improved Gerschgorin Radii Without Eigendecomposition

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Dimension Estimation in Noisy PCA With SURE and Random Matrix Theory

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Analysis of the performance and sensitivity ofeigendecomposition-based detectors

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On the behavior of information theoretic criteria for model orderselection

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On the distribution of the ratio of the largest eigenvalue to the trace of a Wishart matrix

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A theoretical investigation of several model selection criteria for dimensionality reduction

Pattern Recognition Letters

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Abstract

Determining the number of sources from observed data is a fundamental problem in many scientific fields. In this paper 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 present arandommatrix theory viewpoint of the performance of the 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 detection performance and a negligible overestimation probability.