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dc.contributor.advisorEmanuel Parzen
dc.creatorWoodfield, Terry Joe
dc.date.accessioned2020-08-21T21:37:43Z
dc.date.available2020-08-21T21:37:43Z
dc.date.issued1982
dc.identifier.urihttps://hdl.handle.net/1969.1/DISSERTATIONS-394854
dc.descriptionTypescript (photocopy).en
dc.description.abstractA technique for modeling bivariate data that is based on the theory of orthogonal expansions in a separable Hilbert space is examined. A new nonparametric density estimation procedure is developed using an information criterion and is shown to be equivalent to least squares estimation of a density when the criterion function is computed with respect to the empirical distribution function. Computer programs are presented that implement the procedure for the univariate and bivariate cases. Examples utilizing these programs are given and comparisons made to existing density estimation techniques.en
dc.format.extentxi, 260 leavesen
dc.format.mediumelectronicen
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.rightsThis thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use.en
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectStatisticsen
dc.subject.classification1982 Dissertation W887
dc.subject.lcshMultivariate analysisen
dc.subject.lcshHilbert spaceen
dc.titleStatistical modeling of bivariate dataen
dc.typeThesisen
thesis.degree.disciplinePhilosophyen
thesis.degree.grantorTexas A&M Universityen
thesis.degree.nameDoctor of Philosophyen
thesis.degree.namePh. D. in Philosophyen
thesis.degree.levelDoctorialen
dc.contributor.committeeMemberLongnecker, Michael
dc.contributor.committeeMemberSmith, William B.
dc.type.genredissertationsen
dc.type.materialtexten
dc.format.digitalOriginreformatted digitalen
dc.publisher.digitalTexas A&M University. Libraries
dc.identifier.oclc10347688


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