Negative binomial distribution is the most used distribution to model macro-parasite burden in hosts. However reliable maximum likelihood parameter estimation from data is far from trivial. No closed formula is available and numerical estimation requires sophisticated methods. Using data from the literature we show that simple alternatives to negative binomial, like zero-inflated geometric or hurdle geometric distributions, produce a good and even better fit to data than negative binomial distribution. We derived closed simple formulas for the maximum likelihood parameter estimation which constitutes a significant advantage of these distributions over negative binomial distribution.
翻译:负二进制分布是用来模拟主机宏观参数负担的最常用分布方式。 但是,从数据中可靠的最大可能性参数估计远远不是微不足道的。 没有封闭式公式, 数字估计需要复杂的方法。 我们使用文献中的数据显示, 负二进制的简单替代方法, 如零进制几何分布或障碍几何分布, 产生一个比负二进制分布更适合数据的好甚至更好的数据。 我们为最大可能性参数估计得出了封闭式简单公式, 这构成了这些分布相对于负二进制分布的重大优势 。