纵向数据下均值协方差模型的贝叶斯统计诊断
Bayesian Statistical Diagnosis of Joint Mean and Covariance Models with Longitudinal Data
DOI: 10.12677/SA.2020.95094, PDF,    科研立项经费支持
作者: 徐登可：浙江农林大学统计系，浙江 杭州；赵远英：贵阳学院数学与信息科学学院，贵州 贵阳
关键词: 纵向数据；数据删除；Gibbs抽样；MH算法；贝叶斯诊断；Longitudinal Data； Case Deletion； Gibbs Sampler； Metropolis-Hastings Algorithm； Bayesian Diagnosis
摘要: 研究了纵向数据下均值协方差模型的贝叶斯统计诊断。通过应用Gibbs抽样和Metropolis-Hastings (MH)算法相结合的混合算法获得模型贝叶斯数据删除影响诊断统计量来识别数据异常点。模拟研究和实例分析都表明所提出的诊断方法是可行有效的。
Abstract: Bayesian statistical diagnosis of joint mean and covariance models with longitudinal data is studied. By combining the Gibbs sampler and Metropolis-Hastings algorithm, the Bayesian case deletion diagnosis statistic is obtained to identify data outliers. Simulation study and a real data analysis show that the proposed diagnosis method is feasible and effective.
文章引用：徐登可, 赵远英. 纵向数据下均值协方差模型的贝叶斯统计诊断[J]. 统计学与应用, 2020, 9(5): 900-908. https://doi.org/10.12677/SA.2020.95094

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