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dc.contributor.advisorHocking, R. R.
dc.creatorLamotte, Lynn Roy
dc.date.accessioned2020-08-20T19:43:15Z
dc.date.available2020-08-20T19:43:15Z
dc.date.issued1969
dc.identifier.urihttps://hdl.handle.net/1969.1/DISSERTATIONS-174657
dc.description.abstractThis dissertation consists of two parts. In Chapter I an efficient procedure is described for identifying best regression subsets. In Chapter II the likelihood functions for the random, nested and random, classification analysis of variance models are analyzed. An iterative procedure for obtaining maximum likelihood estimates of variance components is described.en
dc.format.extent52 leavesen
dc.format.mediumelectronicen
dc.format.mimetypeapplication/pdf
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.subjectMajor statisticsen
dc.titleSelection of regression variables and contributions to the estimation of variance componentsen
dc.typeThesisen
thesis.degree.disciplineStatisticsen
thesis.degree.grantorTexas A&M Universityen
thesis.degree.nameDoctor of Philosophyen
thesis.degree.namePh. D. in Statisticsen
thesis.degree.levelDoctoralen
thesis.degree.levelDoctorialen
dc.contributor.committeeMemberFreund, R. J.
dc.contributor.committeeMemberHartley, H. O.
dc.contributor.committeeMemberLuther, H. A.
dc.contributor.committeeMemberMoore, Bill C.
dc.type.genredissertationsen
dc.type.materialtexten
dc.format.digitalOriginreformatted digitalen
dc.publisher.digitalTexas A&M University. Libraries
dc.identifier.oclc5717050


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