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NON EFFICIENCY AND NON GAUSSIANITY OF A MAXIMUM LIKELIHOOD ESTIMATOR AT HIGH SIGNAL TO NOISE RATIO AND FINITE NUMBER OF SAMPLES

Abstract : In estimation theory, the asymptotic efficiency of the Maximum Likelihood (ML) method for independent identically distributed observations and when the number T of observations tends to infinity is a well known result. In some scenarii, the number of snapshots may be small making this result unapplicable. In the array processing framework, for Gaussian emitted signals, we fill this lack at high Signal to Noise Ratio (SNR). In this situation, we show that the ML estimation is asymptotically (with respect to SNR) non efficient and non Gaussian.
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https://hal.inria.fr/inria-00444829
Contributor : Alexandre Renaux Connect in order to contact the contributor
Submitted on : Thursday, January 7, 2010 - 12:47:00 PM
Last modification on : Tuesday, April 20, 2021 - 4:54:04 PM
Long-term archiving on: : Friday, June 18, 2010 - 12:31:58 AM

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  • HAL Id : inria-00444829, version 1

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Alexandre Renaux, Philippe Forster, Eric Boyer, Pascal Larzabal. NON EFFICIENCY AND NON GAUSSIANITY OF A MAXIMUM LIKELIHOOD ESTIMATOR AT HIGH SIGNAL TO NOISE RATIO AND FINITE NUMBER OF SAMPLES. IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP-04, 2004, Montreal, Canada. ⟨inria-00444829⟩

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