Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4976439 | Journal of the Franklin Institute | 2007 | 15 Pages |
Abstract
From a number of ML estimators (typically unbiased) of practical interest which include the variance for a Gaussian distribution, the standard deviation for a Laplace distribution, the variance for a Rayleigh distribution and a “spread parameter” for a Cauchy distribution, we design robust estimators according to an emphasis balance between normalized performance and normalized robustness. We measure performance with inverted MSE and robustness with a differential geometric approach.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Hyeon-Cheol Lee, Don R. Halverson,