ZERO-ALTERED AND OTHER REGRESSION-MODELS FOR COUNT DATA WITH ADDED ZEROS
ZERO-ALTERED AND OTHER REGRESSION-MODELS FOR COUNT DATA WITH ADDED ZEROS
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DOI：
10.1002/bimj.4710360505
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发表时间：
1994-01-01
影响因子：
1.7
通讯作者：
HEILBRON, DC
中科院分区：
文献类型：
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作者：
HEILBRON, DC
有时，基于泊松或过度分散的计数分布的广义线性计数模型可能会由于过多的零频率而不能很好地拟合。研究了三种不同类型的回归模型，它们利用了所有的信息并显式地解释了多余的零点，并给出了一般公式。假设了一种简单的加零机制，它直接激励了一种类型的模型，这里称为加零型，其具体形式已由D.Lambert(1992)独立提出，并在作者未发表的工作中发表。提出了一个原始的回归公式(零变模型)，作为计数数据的两部分模型的简化形式，并对其进行了讨论。建议使用两部分模型来帮助开发一个被认为是合适的加零模型。
On occasion, generalized linear models for counts based on Poisson or overdispersed count distributions may encounter lack of fit due to disproportionately large frequencies of zeros. Three alternative types of regression models that utilize all the information and explicitly account for excess zeros are examined and given general formulations. A simple mechanism for added zeros is assumed that directly motivates one type of model, here called the added-zero type, particular forms of which have been proposed independently by D. LAMBERT (1992) and in unpublished work by the author. An original regression formulation (the zero-altered model) is presented as a reduced form of the two-part model for count data, which is also discussed. It is suggested that two-part models be used to aid in development of an added-zero model when the latter is thought to be appropriate.