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dc.contributor.authorD'Ambrosio, Philipen_US
dc.date.accessioned2014-03-14T20:35:08Z
dc.date.available2014-03-14T20:35:08Z
dc.date.issued2008-04-21en_US
dc.identifier.otheretd-05052008-124120en_US
dc.identifier.urihttp://hdl.handle.net/10919/32228
dc.description.abstractRobust estimation of statistical parameters is traditionally believed to exist in a trade space between robustness and efficiency. This thesis examines the Minimum Hellinger Distance Estimator (MHDE), which is known to have desirable robustness properties as well as desirable efficiency properties. This thesis confirms that the MHDE is simultaneously robust against outliers and asymptotically efficient in the univariate location case. Robustness results are then extended to the case of simple linear regression, where the MHDE is shown empirically to have a breakdown point of 50%. A geometric algorithm for solution of the MHDE is developed and implemented. The algorithm utilizes the Riemannian manifold properties of the statistical model to achieve an algorithmic speedup. The MHDE is then applied to an illustrative problem in power system state estimation. The power system is modeled as a structured linear regression problem via a linearized direct current model; robustness results in this context have been investigated and future research areas have been identified from both a statistical perspective as well as an algorithm design standpoint.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartREVISED_FINAL_DAmbrosio_MS_Thesis_27_May_2008b.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectMinimum Hellinger Distanceen_US
dc.subjectPower Systemsen_US
dc.subjectRobust Estimationen_US
dc.subjectInformation Geometryen_US
dc.titleA Differential Geometry-Based Algorithm for Solving the Minimum Hellinger Distance Estimatoren_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
dc.contributor.committeechairMili, Lamine M.en_US
dc.contributor.committeememberWang, Yongen_US
dc.contributor.committeememberBeex, A. A. Louisen_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-05052008-124120/en_US
dc.date.sdate2008-05-05en_US
dc.date.rdate2008-05-28
dc.date.adate2008-05-28en_US


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