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dc.contributor.authorMao, Tian
dc.contributor.authorMao, Tian
dc.date.accessioned2011-04-19T22:26:56Z
dc.date.available2011-04-19T22:26:56Z
dc.date.issued2010
dc.identifier.urihttp://hdl.handle.net/10179/2286
dc.description.abstractMeasuring differences in poverty levels within a country is important for aid allocation. Small area estimates of poverty incidence can be found by combining census and survey data. The usual method uses multiple regression, but an intuitive alternative is to build a classification tree for classifying households as poor or non-poor. This research presents some preliminary results using this method, and compares them to the traditional regression method.en_US
dc.publisherMassey Universityen_US
dc.rightsThe Authoren_US
dc.subjectPoverty statisticsen_US
dc.subjectRegression methodsen_US
dc.titleClassification trees for poverty mapping : a thesis presented in partial fulfilment of the requirements for the degree of Master in Applied Statistics at Massey University, Palmerston North, New Zealanden_US
dc.typeThesisen_US
thesis.degree.disciplineApplied Statistics
thesis.degree.grantorMassey University
thesis.degree.levelMasters
thesis.degree.nameMaster of Applied Statistics (M.Appl.Stat.)


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