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Characterizing the Performance of the Conway-Maxwell Poisson Generalized Linear Model

Authors

  • Royce A. Francis,

    Corresponding author
    1. Department of Engineering Management and Systems Engineering, George Washington University, Washington, DC, USA.
    2. Department of Geography and Environmental Engineering, Johns Hopkins University, Baltimore, MD, USA.
      Department of Engineering Mgmt & Systems Engineering, School of Engineering and Applied Science, 1776 G Street, NW, Washington, DC, USA; seed@gwu.edu.
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  • Srinivas Reddy Geedipally,

    1. Texas Transportation Institute, Texas A&M University, College Station, TX, USA.
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  • Seth D. Guikema,

    1. Department of Geography and Environmental Engineering, Johns Hopkins University, Baltimore, MD, USA.
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  • Soma Sekhar Dhavala,

    1. Department of Statistics, Texas A&M University, College Station, TX, USA.
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  • Dominique Lord,

    1. Zachry Department of Civil Engineering, Texas A&M University, College Station, TX, USA.
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  • Sarah LaRocca

    1. Department of Geography and Environmental Engineering, Johns Hopkins University, Baltimore, MD, USA.
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Department of Engineering Mgmt & Systems Engineering, School of Engineering and Applied Science, 1776 G Street, NW, Washington, DC, USA; seed@gwu.edu.

Abstract

Count data are pervasive in many areas of risk analysis; deaths, adverse health outcomes, infrastructure system failures, and traffic accidents are all recorded as count events, for example. Risk analysts often wish to estimate the probability distribution for the number of discrete events as part of doing a risk assessment. Traditional count data regression models of the type often used in risk assessment for this problem suffer from limitations due to the assumed variance structure. A more flexible model based on the Conway-Maxwell Poisson (COM-Poisson) distribution was recently proposed, a model that has the potential to overcome the limitations of the traditional model. However, the statistical performance of this new model has not yet been fully characterized. This article assesses the performance of a maximum likelihood estimation method for fitting the COM-Poisson generalized linear model (GLM). The objectives of this article are to (1) characterize the parameter estimation accuracy of the MLE implementation of the COM-Poisson GLM, and (2) estimate the prediction accuracy of the COM-Poisson GLM using simulated data sets. The results of the study indicate that the COM-Poisson GLM is flexible enough to model under-, equi-, and overdispersed data sets with different sample mean values. The results also show that the COM-Poisson GLM yields accurate parameter estimates. The COM-Poisson GLM provides a promising and flexible approach for performing count data regression.

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