Bayesian Outlier Detection in Non‐Gaussian Autoregressive Time Series
研究了非高斯自回归时间序列中加性异常值的贝叶斯检测方法，能估计每个时间点异常值发生的概率和大小，帮助识别需要进一步检查的观测值。
This work investigates outlier detection and modelling in non‐Gaussian autoregressive time series models with margins in the class of a convolution closed parametric family. This framework allows for a wide variety of models for count and positive data types. The article investigates additive outliers which do not enter the dynamics of the process but whose presence may adversely influence statistical inference based on the data. The Bayesian approach proposed here allows one to estimate, at each time point, the probability of an outlier occurrence and its corresponding size thus identifying the observations that require further investigation. The methodology is illustrated using simulated and observed data sets.