MODELING MEDIAN HOUSEHOLD INCOME DISTRIBUTION

In 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.

Department dc:contributor.department

Mathematical Statistics

Year dc:date.issued

2008

Author dc:creator

• FOSTER, KEVIN MATTHEW

Advisor dc:contributor.advisor

• KEDEM, BENJAMIN

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/1903/9217

OAI identifier oai:identifier

oai:drum.lib.umd.edu:1903/9217