Title

Comparative Analysis of Dispersion Parameter Estimates in Loglinear Modeling: Applied to E-Commerce Sales and Customer Data

Author(s) Information

Scott Davis, California Polytechnic State University, San Luis ObispoFollow

College - Author 1

College of Science and Mathematics

Department - Author 1

Statistics Department

Degree Name - Author 1

BS in Statistics

Date

9-2012

Primary Advisor

Samuel Frame, Steve Rein

Abstract/Summary

When loglinear models are applied to count data the issue of over-dispersion often arises. Moment and maximum likelihood estimation methods in accounting for over-dispersion are widely used because they allow for model checking tools such as Chi-square, F, and likelihood ratio tests. Here is a comparison between R functions that each uses one method; glm.nb uses MLE, and glm.poisson.disp uses MME. The Index of Dissimilarity and visual model selection (ECDF plots) are also incorporated. These are applied to sales data using product and customer information compiled over the last five years that was generously provided by an e-commerce company.

URL: https://digitalcommons.calpoly.edu/statsp/28