dc.contributor.advisor | Halverson, Don R. | |
dc.creator | Bhagawat, Pankaj | |
dc.date.accessioned | 2004-09-30T01:42:27Z | |
dc.date.available | 2004-09-30T01:42:27Z | |
dc.date.created | 2003-08 | |
dc.date.issued | 2004-09-30 | |
dc.identifier.uri | https://hdl.handle.net/1969.1/107 | |
dc.description.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. | en |
dc.format.extent | 827488 bytes | en |
dc.format.extent | 63481 bytes | en |
dc.format.medium | electronic | en |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Texas A&M University | |
dc.subject | Performance | en |
dc.subject | Robustness | en |
dc.subject | cost function | en |
dc.title | Design of a robust parameter estimator for nominally Laplacian noise | en |
dc.type | Book | en |
dc.type | Thesis | en |
thesis.degree.department | Electrical Engineering | en |
thesis.degree.discipline | Electrical Engineering | en |
thesis.degree.grantor | Texas A&M University | en |
thesis.degree.name | Master of Science | en |
thesis.degree.level | Masters | en |
dc.contributor.committeeMember | Georghiades, Costas N. | |
dc.contributor.committeeMember | Chang, Kai | |
dc.contributor.committeeMember | Friesen, Donald | |
dc.type.genre | Electronic Thesis | en |
dc.type.material | text | en |
dc.format.digitalOrigin | born digital | en |