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Activity Number: 176 - Modeling
Type: Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract #330257
Title: Mean-Parametrized Conway-Maxwell-Poisson Regression Models for Dispersed Counts
Author(s): Ho Ting Fung* and Alan Huang and Aya Alwan and Justin Wishart
Companies: Macquarie University and University of Queensland and Macquarie University and Macquarie University
Keywords: Count data; Generalized linear models; Overdispersion; Underdispersion; Conway-Maxwell-Poisson distribution; Using R

Conway-Maxwell-Poisson (CMP) distributions are flexible generalizations of the Poisson distribution for modelling overdispersed or underdispersed counts. The main hindrance to their wider use in practice seems to be the inability to directly model the mean of counts, making them not compatible with nor comparable to competing count regression models, such as the log-linear Poisson, negative-binomial or generalized Poisson regression models. In this talk, we will illustrates how CMP distributions can be parametrized via the mean, so that simpler and more easily interpretable mean-models can be used, such as a log-linear model. Moreover, this talk introduces the R package which provides a collection of functions for estimation, testing and diagnostic checking for the proposed model. The performance of the R routine against the earlier proposed MATLAB routine will also be discussed.

Authors who are presenting talks have a * after their name.

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