The estimation of missing values in hydrological records using the EM algorithm and regression methods

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dc.contributor.advisorZucchini, Walteren_ZA
dc.contributor.advisorSparks, Ross Sen_ZA
dc.contributor.authorMakhuvha, Tondanien_ZA
dc.date.accessioned2016-02-18T12:16:10Z
dc.date.available2016-02-18T12:16:10Z
dc.date.issued1988en_ZA
dc.descriptionIncludes bibliography.en_ZA
dc.description.abstractThe objective of this thesis is to review existing methods for estimating missing values in rainfall records and to propose a number of new procedures. Two classes of methods are considered. The first is based on the theory of variable selection in regression. Here the emphasis is on finding efficient methods to identify the set of control stations which are likely to yield the best regression estimates of the missing values in the target station. The second class of methods is based on the EM algorithm, proposed by Dempster, Laird and Rubin (1977). The emphasis here is to estimate the missing values directly without first making a detailed selection of control stations. All "relevant" stations are included. This method has not previously been applied in the context of estimating missing rainfall values.en_ZA
dc.identifier.apacitationMakhuvha, T. (1988). <i>The estimation of missing values in hydrological records using the EM algorithm and regression methods</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/17120en_ZA
dc.identifier.chicagocitationMakhuvha, Tondani. <i>"The estimation of missing values in hydrological records using the EM algorithm and regression methods."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 1988. http://hdl.handle.net/11427/17120en_ZA
dc.identifier.citationMakhuvha, T. 1988. The estimation of missing values in hydrological records using the EM algorithm and regression methods. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Makhuvha, Tondani AB - The objective of this thesis is to review existing methods for estimating missing values in rainfall records and to propose a number of new procedures. Two classes of methods are considered. The first is based on the theory of variable selection in regression. Here the emphasis is on finding efficient methods to identify the set of control stations which are likely to yield the best regression estimates of the missing values in the target station. The second class of methods is based on the EM algorithm, proposed by Dempster, Laird and Rubin (1977). The emphasis here is to estimate the missing values directly without first making a detailed selection of control stations. All "relevant" stations are included. This method has not previously been applied in the context of estimating missing rainfall values. DA - 1988 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 1988 T1 - The estimation of missing values in hydrological records using the EM algorithm and regression methods TI - The estimation of missing values in hydrological records using the EM algorithm and regression methods UR - http://hdl.handle.net/11427/17120 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/17120
dc.identifier.vancouvercitationMakhuvha T. The estimation of missing values in hydrological records using the EM algorithm and regression methods. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 1988 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/17120en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Statistical Sciencesen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherRain and rainfall - Statistical methodsen_ZA
dc.subject.otherAlgorithmsen_ZA
dc.subject.otherRegression analysisen_ZA
dc.titleThe estimation of missing values in hydrological records using the EM algorithm and regression methodsen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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