Bayesian zero-inflated generalized Poisson regression model: estimation and case influence diagnostics
Bayesian zero-inflated generalized Poisson regression model: estimation and case influence diagnostics
复制标题
贝叶斯零膨胀广义泊松回归模型：估计和案例影响诊断
DOI：
10.1080/02664763.2013.871508
复制
发表时间：
2014-06-03
影响因子：
1.5
通讯作者：
Wei, Bo-Cheng
中科院分区：
文献类型：
--
作者：
Xie, Feng-Chang;Lin, Jin-Guan;Wei, Bo-Cheng
带过多零的计数数据在许多情况下都会出现。在这里，我们关注的是为零膨胀广义泊松(ZIGP)回归模型开发贝叶斯分析来解决这个问题。该模型是对零膨胀泊松模型的有效推广，因为广义泊松分布相对于泊松分布是过度离散/欠离散的。由于ZIGP模型的复杂性，采用马尔科夫链蒙特卡罗方法对所考虑的模型进行了贝叶斯分析。此外，对模型的选择标准进行了讨论，并研究了基于Kullback-Leibler散度的联合后验分布的贝叶斯病例删除影响诊断。最后，给出了一个模拟研究和一个心理学例子来说明我们的方法。
Count data with excess zeros arises in many contexts. Here our concern is to develop a Bayesian analysis for the zero-inflated generalized Poisson (ZIGP) regression model to address this problem. This model provides a useful generalization of zero-inflated Poisson model since the generalized Poisson distribution is overdispersed/underdispersed relative to Poisson. Due to the complexity of the ZIGP model, Markov chain Monte Carlo methods are used to develop a Bayesian procedure for the considered model. Additionally, some discussions on the model selection criteria are presented and a Bayesian case deletion influence diagnostics is investigated for the joint posterior distribution based on the Kullback–Leibler divergence. Finally, a simulation study and a psychological example are given to illustrate our methodology.