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首页> 外文期刊>Journal of applied statistics >Analyzing clustered count data with a cluster-specific random effect zero-inflated Conway-Maxwell-Poisson distribution
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Analyzing clustered count data with a cluster-specific random effect zero-inflated Conway-Maxwell-Poisson distribution

机译：使用特定于群集的随机效应零膨胀的Conway-Maxwell-Poisson分布分析群集计数数据

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摘要

Count data analysis techniques have been developed in biological and medical research areas. In particular, zero-inflated versions of parametric count distributions have been used to model excessive zeros that are often present in these assays. The most common count distributions for analyzing such data are Poisson and negative binomial. However, a Poisson distribution can only handle equidispersed data and a negative binomial distribution can only cope with overdispersion. However, a Conway-Maxwell-Poisson (CMP) distribution [4] can handle a wide range of dispersion. We show, with an illustrative data set on next-generation sequencing of maize hybrids, that both underdispersion and overdispersion can be present in genomic data. Furthermore, the maize data set consists of clustered observations and, therefore, we develop inference procedures for a zero-inflated CMP regression that incorporates a cluster-specific random effect term. Unlike the Gaussian models, the underlying likelihood is computationally challenging. We use a numerical approximation via a Gaussian quadrature to circumvent this issue. A test for checking zero-inflation has also been developed in our setting. Finite sample properties of our estimators and test have been investigated by extensive simulations. Finally, the statistical methodology has been applied to analyze the maize data mentioned before.
机译：在生物学和医学研究领域已经开发了计数数据分析技术。特别是，参数计数分布的零膨胀版本已用于对这些分析中经常出现的过多零点进行建模。用于分析此类数据的最常见计数分布是泊松和负二项式。但是，泊松分布只能处理等散数据，而负二项式分布只能应对过度分散。但是，Conway-Maxwell-Poisson（CMP）分布[4]可以处理广泛的色散。我们用玉米杂交种的下一代测序的说明性数据集显示，基因组数据中可能存在分散不足和过度分散的情况。此外，玉米数据集由聚类观察组成，因此，我们开发了针对零膨胀CMP回归的推理程序，该回归程序结合了特定于聚类的随机效应项。与高斯模型不同，潜在的可能性在计算上具有挑战性。我们通过高斯积分使用数值逼近来规避此问题。我们的环境中还开发了一种检查零通货膨胀的测试。我们的估计器和测试的有限样本属性已通过广泛的模拟进行了研究。最后，统计方法已用于分析前面提到的玉米数据。

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