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Estimation of Parameters for Gaussian Random Variables using Robust Differential Geometric Techniques

Most signal processing systems today need to estimate parameters of the underlying
probability distribution, however quantifying the robustness of this system has
always been difficult. This thesis attempts to quantify the performance and robustness
of the Maximum Likelihood Estimator (MLE), and a robust estimator, which
is a Huber-type censored form of the MLE. This is possible using diff erential geometric
concepts of slope. We compare the performance and robustness of the robust
estimator, and its behaviour as compared to the MLE. Various nominal values of
the parameters are assumed, and the performance and robustness plots are plotted.
The results showed that the robustness was high for high values of censoring and
was lower as the censoring value decreased. This choice of the censoring value was
simplifi ed since there was an optimum value found for every set of parameters. This
study helps in future studies which require quantifying robustness for di fferent kinds
of estimators.
Date16 January 2010
CreatorsYellapantula, Sudha
ContributorsHalverson, Don R.
Source SetsTexas A and M University
Detected LanguageEnglish
TypeBook, Thesis, Electronic Thesis

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