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dc.contributor.authorKim, Donggeonen_US
dc.date.accessioned2014-03-14T21:09:28Z
dc.date.available2014-03-14T21:09:28Z
dc.date.issued1995-02-13en_US
dc.identifier.otheretd-02132009-171622en_US
dc.identifier.urihttp://hdl.handle.net/10919/37348
dc.description.abstractThe Least Squares Mixture Decomposition Estimator (LSMDE) is a new nonparametric density estimation technique developed by modifying the ordinary kernel density estimators. While the ordinary kernel density estimator assumes equal weight (l/n) for each data point, LSMDE assigns the optimized weight to each data point via the quadratic programming under the Mean Integrated Squared Error (MISE) criterion. As results, we find out that the optimized weights for a given data set are far different from l/n for a reasonable smoothing parameter and, furthermore, many data points are assigned to zero weights after the optimization. This implies that LSMDE decomposes the underlying density function to a finite mixture distribution of p (< n) kernel functions. LSMDE turns out to be more informative, especially in multi-dimensional cases when the visualization of the density function is difficult, than the ordinary kernel density estimator by suggesting the underlying structure of a given data set.en_US
dc.format.mediumBTDen_US
dc.publisherVirginia Techen_US
dc.relation.haspartLD5655.V856_1995.K56.pdfen_US
dc.subjectestimatorsen_US
dc.subject.lccLD5655.V856 1995.K56en_US
dc.titleLeast squares mixture decomposition estimationen_US
dc.typeDissertationen_US
dc.contributor.departmentStatisticsen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineStatisticsen_US
dc.contributor.committeechairTerrell, George R.en_US
dc.contributor.committeememberGood, I. J.en_US
dc.contributor.committeememberFoutz, Roberten_US
dc.contributor.committeememberSmith, Eric P.en_US
dc.contributor.committeememberCoakley, Clint W.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-02132009-171622/en_US
dc.date.sdate2009-02-13en_US
dc.date.rdate2009-02-13
dc.date.adate2009-02-13en_US


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