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.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/107 |
| Date | 30 September 2004 |
| Creators | Bhagawat, Pankaj |
| Contributors | Halverson, Don R. |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | 827488 bytes, 63481 bytes, electronic, application/pdf, text/plain, born digital |
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