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Marginalized mixture models for count data from multiple source populations.
Journal of Statistical Distributions and Applications Pub Date : 2017-04-07 , DOI: 10.1186/s40488-017-0057-4
Habtamu K Benecha 1 , Brian Neelon 2 , Kimon Divaris 3 , John S Preisser 4
Affiliation  

Mixture distributions provide flexibility in modeling data collected from populations having unexplained heterogeneity. While interpretations of regression parameters from traditional finite mixture models are specific to unobserved subpopulations or latent classes, investigators are often interested in making inferences about the marginal mean of a count variable in the overall population. Recently, marginal mean regression modeling procedures for zero-inflated count outcomes have been introduced within the framework of maximum likelihood estimation of zero-inflated Poisson and negative binomial regression models. In this article, we propose marginalized mixture regression models based on two-component mixtures of non-degenerate count data distributions that provide directly interpretable estimates of exposure effects on the overall population mean of a count outcome. The models are examined using simulations and applied to two datasets, one from a double-blind dental caries incidence trial, and the other from a horticultural experiment. The finite sample performance of the proposed models are compared with each other and with marginalized zero-inflated count models, as well as ordinary Poisson and negative binomial regression.

中文翻译:

边缘混合模型,用于来自多个源种群的计数数据。

混合物分布为从具有无法解释的异质性的种群收集的数据建模提供了灵活性。尽管传统的有限混合模型对回归参数的解释特定于未观察到的亚人群或潜在类别,但研究人员通常对推断总体中计数变量的边际平均值感兴趣。最近,在零膨胀泊松和负二项回归模型的最大似然估计框架内引入了零膨胀计数结果的边际均值回归建模程序。在这篇文章中,我们提出了基于非退化计数数据分布的两成分混合的边际混合回归模型,该模型可以直接解释暴露对计数结果总体总体平均值的影响。使用模拟检查模型,并将其应用于两个数据集,一个来自双盲龋齿发病率试验,另一个来自园艺实验。所提出的模型的有限样本性能相互比较,并与边缘化的零膨胀计数模型进行了比较,以及普通的泊松和负二项式回归。
更新日期:2017-04-07
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