摘要
在公共卫生等应用领域,经常会同时出现零观测值、一观测值较多的情况.为更好地拟合这类数据,采用0−1膨胀负二项分布及其回归模型进行分析.在数据扩充基础上,结合Pólya−Gamma潜变量对模型参数进行贝叶斯推断.最后,对我国湖北省2019冠状病毒病(COVID−19)死亡数据集进行分析.研究表明,0−1膨胀负二项回归模型能够达到更好的拟合效果.
Count datas with excess zeros and ones arise frequently in the field of public health.In order to fit the kind of data,a zero-and-one-inflated negative binomial(ZOINB)distribution and its regression model were adopted for analysis.Based on data augmentation strategy and Pólya−Gamma latent variables Bayesian inference was used to estimate the parameters of ZOINB regression model.Finally,one corona virus disease 2019(COVID−19)death data-set from Hubei Province in China was analyzed.The result illustrates that ZOINB regression model can achieve better fitting effect.
作者
马巧玲
肖翔
MA Qiaoling;XIAO Xiang(School of Mathematics,Physics and Statistics,Shanghai University of Engineering Science,Shanghai 201620,China)
出处
《上海工程技术大学学报》
CAS
2022年第2期212-217,共6页
Journal of Shanghai University of Engineering Science
基金
全国统计科学研究项目资助(2020LY080)。
