论文标题
零膨胀的泊松群集加权模型：属性和应用
Zero-Inflated Poisson Cluster-Weighted Models: Properties and Applications
论文作者
论文摘要
在本文中，我向称为零膨胀的Poisson CWMS（ZIPCWM）的群集加权模型（CWMS）的家族提出了一类新的零泄漏的泊松模型。 ZIPCWM扩展了泊松簇加权模型和其他混合模型。我通过迭代重新加权的最小二乘算法提出了一种期望最大化（EM）算法。 I从理论上和分析地通过广泛的模拟研究从理论上和分析研究了所提出的模型的可识别性。参数恢复，分类评估和不同信息标准的性能通过广泛的模拟设计研究。 ZIPCWM应用于实际数据，该数据占$ 40 \％$的超过零的超过零。我们在数据上探讨了ZipCWM，固定零镀金泊松混合模型（FZIP）和Poisson簇加权模型（PCWM）的分类性能。基于混乱矩阵，ZIPCWM达到$ 97.4 \％$分类功率，PCWM可实现$ 67.30 \％$，而FZIP的分类性能最差。总之，ZIPCWM的表现均优于PCWM和FZIP模型。
In this paper, I propose a new class of Zero-Inflated Poisson models into the family of Cluster Weighted Models (CWMs) called Zero-Inflated Poisson CWMs (ZIPCWM). ZIPCWM extends Poisson cluster weighted models and other mixture models. I propose an Expectation-Maximization (EM) algorithm via an iteratively reweighted least squares for the model. I theoretically and analytically investigate the identifiability of the proposed model through an extensive simulation study. Parameter recovery, classification assessment, and performance of different information criteria are investigated through broad simulation design. ZIPCWM is applied to real data which accounts for excess zeros of over $40\%$. We explore the classification performance of ZIPCWM, Fixed Zero-inflated Poisson mixture model (FZIP), and Poisson cluster weighted model (PCWM) on the data. Based on the confusion matrix, ZIPCWM achieves $97.4\%$ classification power, PCWM achieves $67.30\%$, while FZIP has the worst classification performance. In conclusion, ZIPCWM outperforms both PCWM and FZIP models.
