首页> 外文期刊>Biometrics: Journal of the Biometric Society : An International Society Devoted to the Mathematical and Statistical Aspects of Biology >Modeling the dependence between number of trials and success probability in beta-binomial-Poisson mixture distributions.
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Modeling the dependence between number of trials and success probability in beta-binomial-Poisson mixture distributions.

机译:在β-二项式-泊松混合分布中模拟试验次数与成功概率之间的依存关系。

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摘要

Beta-binomial models are widely used for overdispersed binomial data, with the binomial success probability modeled as following a beta distribution. The number of binary trials in each binomial is assumed to be nonrandom and unrelated to the success probability. In many behavioral studies, however, binomial observations demonstrate more complex structures. In this article, a general beta-binomial-Poisson mixture model is developed, to allow for a relation between the number of trials and the success probability for overdispersed binomial data. An EM algorithm is implemented to compute both the maximum likelihood estimates of the model parameters and the corresponding standard errors. For illustration, the methodology is applied to study the feeding behavior of green-backed herons in two southeastern Missouri streams.
机译:Beta二项式模型被广泛用于过度分散的二项式数据,其二项式成功概率建模为遵循Beta分布。假定每个二项式中的二元试验次数是非随机的,并且与成功概率无关。但是,在许多行为研究中,二项式观察都显示出更复杂的结构。在本文中,开发了一个通用的β-二项式-泊松混合模型,以允许试验次数与过度分散的二项式数据的成功概率之间的关系。实施EM算法以计算模型参数的最大似然估计和相应的标准误差。为了说明这一点,该方法被用于研究密苏里州东南部两条小溪中绿背鹭的摄食行为。

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