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

机译：表征Conway-Maxwell Poisson广义线性模型的性能

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摘要

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.
机译：计数数据在风险分析的许多领域无处不在。例如，死亡，不良健康后果，基础设施系统故障和交通事故都记录为计数事件。风险分析师通常希望估计离散事件数量的概率分布，作为进行风险评估的一部分。由于假设的方差结构，通常在风险评估中经常使用的这种类型的传统计数数据回归模型存在局限性。最近，提出了一种基于Conway-Maxwell Poisson（COM-Poisson）分布的更灵活的模型，该模型具有克服传统模型局限性的潜力。但是，此新模型的统计性能尚未完全表征。本文评估了拟合COM-Poisson广义线性模型（GLM）的最大似然估计方法的性能。本文的目的是（1）表征COM-Poisson GLM的MLE实现的参数估计精度，以及（2）使用模拟数据集估计COM-Poisson GLM的预测精度。研究结果表明，COM-Poisson GLM具有足够的灵活性，可以用不同的样本平均值对欠分散，均匀和过度分散的数据集进行建模。结果还表明，COM-Poisson GLM可以得出准确的参数估计值。 COM-Poisson GLM提供了一种有希望且灵活的方法来执行计数数据回归。

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  • 来源
    《Risk analysis》 |2012年第1期|p.167-183|共17页
  • 作者

    Royce A. Francis; Srinivas Reddy Geedipally; Seth D. Guikema; Soma Sekhar Dhavala; Dominique Lord; Sarah LaRocca;

  • 作者单位

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

    Texas Transportation Institute, Texas A&M University, College Station, TX, USA;

    Department of Geography and Environmental Engineering, Johns Hopkins University, Baltimore, MD, USA;

    Department of Statistics, Texas A&M University, College Station, TX, USA;

    Zachry Department of Civil Engineering, Texas A&M University, College Station, TX, USA;

    Department of Geography and Environmental Engineering, Johns Hopkins University, Baltimore, MD, USA;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    maximum likelihood estimation; overdispersed count data; regression; underdispersed count data;

    机译：最大似然估计;分散的计数数据;回归分散计数数据;

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