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dc.contributor.supervisor Leblanc, Alexandre (Statistics) Durocher, Stephane (Computer Science) en_US
dc.contributor.author Ramsay, Kelly
dc.date.accessioned 2018-04-12T18:41:51Z
dc.date.available 2018-04-12T18:41:51Z
dc.date.issued 2017
dc.date.submitted 2018-03-27T18:37:54Z en
dc.identifier.uri http://hdl.handle.net/1993/32967
dc.description.abstract This thesis concerns select methods related to multivariate nonparametric data description, especially multivariate location. It presents and provides implementations of algorithms for computing the projection median both exactly (in low dimensions) and approximately (for use in higher dimensions). The algorithms use techniques from computational geometry and Monte Carlo methods. Further, an intuitive notion of data depth based on an average univariate ranking of points is introduced. This depth measure is shown to be quickly computable in low dimensions and easily approximated in high dimensions via Monte Carlo techniques. In addition, its theoretical properties are investigated. Several applications of these methods are demonstrated, using both real and simulated data. en_US
dc.subject Statistics en_US
dc.subject Robust statistics en_US
dc.subject Multivariate en_US
dc.subject Median en_US
dc.title Computable, robust multivariate location using integrated univariate ranks en_US
dc.degree.discipline Statistics en_US
dc.contributor.examiningcommittee Jafari Jozani, Mohammad (Statistics) Gunderson, Karen (Mathematics) en_US
dc.degree.level Master of Science (M.Sc.) en_US
dc.description.note May 2018 en_US


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