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 , 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.
|Title of host publication||2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings|
|State||Published - 2006|
|Event||2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France|
Duration: 14 May 2006 → 19 May 2006
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Conference||2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006|
|Period||14/05/06 → 19/05/06|