Classification 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 Zealand

dc.contributor.author	Mao, Tian
dc.contributor.author	Mao, Tian
dc.date.accessioned	2011-04-19T22:26:56Z
dc.date.available	2011-04-19T22:26:56Z
dc.date.issued	2010
dc.description.abstract	Measuring 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.identifier.uri	http://hdl.handle.net/10179/2286
dc.publisher	Massey University	en_US
dc.rights	The Author	en_US
dc.subject	Poverty statistics	en_US
dc.subject	Regression methods	en_US
dc.title	Classification 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 Zealand	en_US
dc.type	Thesis	en_US
thesis.degree.discipline	Applied Statistics
thesis.degree.grantor	Massey University
thesis.degree.level	Masters
thesis.degree.name	Master of Applied Statistics (M.Appl.Stat.)

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