On Maximum Likelihood Estimation in the Presence of Vanishing Information Measure

Autor:	Anthony J. Weiss, O. Landau
Rok vydání:	2006
Předmět:	Bayes estimator Minimum-variance unbiased estimator Efficient estimator Bias of an estimator Estimation theory Stein's unbiased risk estimate Statistics Applied mathematics Estimator Statistics::Other Statistics Cramér–Rao bound Mathematics
Zdroj:	ICASSP (3)
DOI:	10.1109/icassp.2006.1660745
Popis:	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 Cramer-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 Cramer-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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9436e946bfbc36177efa4c55ec215484 https://doi.org/10.1109/icassp.2006.1660745 Zobrazit plný text záznamu