The inference procedure under generalized linear model breaks down when there exists too many zeros in count data that the parent distribution cannot accommodate. We argue that the process of separating ‘bad' zeros through so-called zero inflated count model will lead to a less optimal estimators of the regression parameters than that obtained by fitting zero truncated distribution after discarding ‘all’ zeros. In this paper extensive simulation studies have been carried out to compare performance of fitting zero-truncated Poisson (ZTP), Poisson hurdle (PH) and zero-inflated Poisson (ZIP) models when the data generating process is either ZIP or PH. This study reveals the fact that for analyzing Poisson count data subject to excess zeros, instead of fitting a zero-inflated model, the traditional ZTP would be the best choice. As an illustration, results have been compared by analyzing antenatal care seeking behavior data extracted from a demographic health survey of Bangladesh.

当计数数据中存在太多父分布无法容纳的零时，广义线性模型下的推理过程就会崩溃。我们认为，通过所谓的零膨胀计数模型分离“坏”零的过程将导致回归参数的最佳估计量不如通过在丢弃“全”零后拟合零截断分布所获得的估计量。在本文中，进行了广泛的模拟研究，以比较在数据生成过程为 ZIP 或 PH 时拟合零截断泊松 (ZTP)、泊松障碍 (PH) 和零膨胀泊松 (ZIP) 模型的性能。这项研究揭示了一个事实，即对于分析过度零的泊松计数数据，传统的 ZTP 将是最佳选择，而不是拟合零膨胀模型。作为例证，