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MODELING MEDIAN HOUSEHOLD INCOME DISTRIBUTION

dc.contributor.advisorKEDEM, BENJAMINen_US
dc.contributor.authorFOSTER, KEVIN MATTHEWen_US
dc.date.accessioned2009-07-02T05:56:22Z
dc.date.available2009-07-02T05:56:22Z
dc.date.issued2008en_US
dc.identifier.urihttp://hdl.handle.net/1903/9217
dc.description.abstractIn this thesis we are going to use U.S. Census data to study median household income distribution for 13 U.S. counties and seven U.S. states. Over the years, researchers have fitted income data with various probability distributions. During our review of the literature, we saw that researchers do not agree on any one best distribution. We will be looking at lognormal, gamma and Weibull, each of which has two parameters. We will also investigate the Singh-Maddala, which has three parameters. Finally, we will introduce the Generalized Beta II, which has four parameters. These distributions will be tested using Mean Squared Error, Mean Absolute Error, Chi-square Goodness-Of-Fit, Akaike's Information Criterion and Bayesian Information Criterion. We also use the graphical technique of QQ Plots. We discover that the Singh-Maddala most often provides the best fit model for our income data, and we make the recommendation that users choose the Singh-Maddala distribution as their model when studying median household income distribution.en_US
dc.format.extent357051 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.titleMODELING MEDIAN HOUSEHOLD INCOME DISTRIBUTIONen_US
dc.typeThesisen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentMathematical Statisticsen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pquncontrolledCENSUSen_US
dc.subject.pquncontrolledDISTRIBUTIONen_US
dc.subject.pquncontrolledGAMMAen_US
dc.subject.pquncontrolledINCOMEen_US
dc.subject.pquncontrolledLOGNORMALen_US
dc.subject.pquncontrolledSINGH-MADDALAen_US


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