Design of a robust parameter estimator for nominally Laplacian noise
Abstract
In this work we have made use of a geometric approach which quantifies robustness and performance and we finally combine them using a cost function. In particular, we calculate the robustness
of the estimate of standard deviation of nominally Laplacian distribution. As this distribution is imperfectly known,
we employ a more general family, the generalized Gaussian; Laplacian distribution, is one of the members of this family.
We compute parameter estimates and present a classical algorithm which is then analyzed for distribution from the generalized Gaussian family.
We calculate the mean squared error according to the censoring height k.
We measure performance as a function of (1/MSE) and combine it with robustness using a cost criterion and design
a robust estimator which optimizes a mix of performance and robustness specified by the user.
Citation
Bhagawat, Pankaj (2003). Design of a robust parameter estimator for nominally Laplacian noise. Master's thesis, Texas A&M University. Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /107.