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dc.contributor.supervisorLeblanc, Alexandre (Statistics)en_US
dc.contributor.authorStinner, Mark
dc.date.accessioned2013-08-26T17:00:24Z
dc.date.available2013-08-26T17:00:24Z
dc.date.issued2013-08-26
dc.identifier.urihttp://hdl.handle.net/1993/22110
dc.description.abstractA technique for establishing L1 asymptotic unbiasedness of a kernel density estimator in Rd that does not depend on the form of the kernel function will be demonstrated. We will introduce the concept of a region sequence of a sequence of kernel functions and show how this can be used to give necessary and sufficient conditions for L1 asymptotic unbiasedness. These results are then applied to kernel density estimators whose form is given and a number of known and novel results are obtained.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectasymptoticen_US
dc.subjectunbiasednessen_US
dc.subjectkernelen_US
dc.subjectdensityen_US
dc.subjectestimatoren_US
dc.titleA general approach to the study of L1 asymptotic unbiasedness of kernel density estimators in Rden_US
dc.typeinfo:eu-repo/semantics/masterThesis
dc.typemaster thesisen_US
dc.degree.disciplineStatisticsen_US
dc.contributor.examiningcommitteeKoulis, Theodoro (Statistics) Prymak, Andriy (Mathematics)en_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.noteOctober 2013en_US


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