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。
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.
出处
《统计学与应用》
2020年第5期900-908,共9页
Statistical and Application
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
