Date of Award
2016
Document Type
Open Access Thesis
Department
Statistics
Sub-Department
The Norman J. Arnold School of Public Health
First Advisor
James W. Hardin
Abstract
This paper explores the double Poisson distribution. The probability mass function and the difficulties associated with derivative-based optimization for this distribution are discussed. Stata software developed for estimation of double Poisson regression is detailed. Simulations are used to test the software. Data which are over-, under-, and equidispersed relative to the Poisson are generated and the software is utilized to estimate a regression model, a zero-inflated model, and a marginalized zero-inflated model all based on the double Poisson distribution. The estimated power of the test for φ = 1 for the double Poisson models are compared to the power of the test for α = 0 for the negative binomial models. Coefficient estimation is also compared across the models.
Rights
© 2016, Rebecca Wardrop
Recommended Citation
Wardrop, R.(2016). Regression Models for Count Data Based on the Double Poisson Distribution. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/3870