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dc.contributor.advisorSmith, William B.
dc.creatorAnder, Henry Fred
dc.date.accessioned2020-08-21T21:35:18Z
dc.date.available2020-08-21T21:35:18Z
dc.date.issued1977
dc.identifier.urihttps://hdl.handle.net/1969.1/DISSERTATIONS-369402
dc.descriptionVita.en
dc.description.abstractA variate transformation model is employed to estimate the percentage of a large land segment covered by a particular crop based on spectral intensity data from National Aeronautical Space Administration's Earth Resources Technological Satellite. The procedure is based on a transformational of a mixed distribution of crop "signatures" into their known marginal distributions. The parameter estimates for the transformation are obtained simultaneously with the proportion of each crop type of interest on the land segment. Monte Carlo studies are made to investigate the properties of the estimates and the effectiveness of the solution methods applied.en
dc.format.extentviii, 70 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.subjectStatistical hypothesis testingen
dc.subjectStatisticsen
dc.subject.classification1977 Dissertation A543
dc.subject.lcshStatistical hypothesis testingen
dc.titleConstrained nonlinear estimation applied to earth resources satellite dataen
dc.typeThesisen
thesis.degree.grantorTexas A&M Universityen
thesis.degree.nameDoctor of Philosophyen
dc.contributor.committeeMemberCurry, Guy
dc.contributor.committeeMemberKshirsagar, A. M.
dc.contributor.committeeMemberRinger, Larry
dc.type.genredissertationsen
dc.type.materialtexten
dc.format.digitalOriginreformatted digitalen
dc.publisher.digitalTexas A&M University. Libraries
dc.identifier.oclc3615961


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