On maximum likelihood estimation in the presence of vanishing information measure

Ori Landau, Anthony J. Weiss

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We analyze the parameter estimation Mean Square Error when the Fisher Information Measure is zero at some points within the parameter space. At these points the Cramér-Rao Lower Bound diverges and no unbiased estimator can achieve a finite Mean Square Error. Under mild regularity conditions the Maximum Likelihood Estimator is known to be asymptotically unbiased and therefore lower bounded by the Cramér-Rao Lower Bound [1], It is therefore of interest to examine the Maximum Likelihood Estimator performance in the presence of vanishing Fisher Information Measure. We provide new theoretical and practical results. All results are corroborated by simulations.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
PagesIII680-III683
StatePublished - 2006
Event2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France
Duration: 14 May 200619 May 2006

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume3
ISSN (Print)1520-6149

Conference

Conference2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
Country/TerritoryFrance
CityToulouse
Period14/05/0619/05/06

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