A diagnostic function to examine candidate distributions to model univariate data

Date

2010-05-10T13:31:52Z

Journal Title

Journal ISSN

Volume Title

Publisher

Kansas State University

Abstract

To help with identifying distributions to effectively model univariate continuous data, the R function diagnostic is proposed. The function will aid in determining reasonable candidate distributions that the data may have come from. It uses a combination of the Pearson goodness of fit statistic, Anderson-Darling statistic, Lin’s concordance correlation between the theoretical quantiles and observed quantiles, and the maximum difference between the theoretical quantiles and the observed quantiles. The function generates reasonable candidate distributions, QQ plots, and histograms with superimposed density curves. When a simulation study was done, the function worked adequately; however, it was also found that many of the distributions look very similar if the parameters are chosen carefully. The function was then used to attempt to decipher which distribution could be used to model weekly grocery expenditures of a family household.

Description

Keywords

statistics, R, statistical computing, goodness of fit, probability distributions, diagnostic

Graduation Month

May

Degree

Master of Science

Department

Department of Statistics

Major Professor

Suzanne Dubnicka

Date

2010

Type

Report

Citation