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dc.contributor.advisorArıkan, Orhanen_US
dc.contributor.authorChebil, Fehmien_US
dc.date.accessioned2016-01-08T20:15:04Z
dc.date.available2016-01-08T20:15:04Z
dc.date.issued1997
dc.identifier.urihttp://hdl.handle.net/11693/17970
dc.descriptionAnkara : The Department of Electrical and Electronics Engineering and Institute of Engineering and Science, Bilkent Univ., 1997.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 1997.en_US
dc.descriptionIncludes bibliographical references leaves 69-71en_US
dc.description.abstractRobust methods of estimation of unknowns in a linear system of equations with modeling uncertainties are proposed. Specifically, when the uncertainty in the model is limited to the statistics of the additive noise, algorithms based on adaptive regularized techniques are introduced and compared with commonly used estimators. It is observed that significant improvements can be achieved at low signal-to-noise ratios. Then, we investigated the case of a parametric uncertainty in the model matrix and proposed algorithms based on non-linear ridge regression, maximum likelihood and Bayesian estimation that can be used depending on the amount of prior information. Based on a detailed comparison study between the proposed and available methods, it is shown that the new approaches provide significantly better estimates for the unknowns in the presence of model uncertainties.en_US
dc.description.statementofresponsibilityChebil, Fehmien_US
dc.format.extentxiv, 71 leavesen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectRobust Estimcitionen_US
dc.subjectParametric measurement uncertaintiesen_US
dc.subjectRidge Regression,en_US
dc.subjectWavelet based reconstructionen_US
dc.subjectMeciri Sqiuire Erroren_US
dc.subject.lccQA276.8 .C44 1997en_US
dc.subject.lcshRobust statistics.en_US
dc.subject.lcshEstimation theory.en_US
dc.subject.lcshParameter estimation.en_US
dc.subject.lcshRidge regression.en_US
dc.titleRobust estimation of unknowns in a linear system of equations with modeling uncertaintiesen_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US


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