Trend and Variance Adaptive Bayesian Changepoint Analysis and Local Outlier Scoring
提出一种自适应贝叶斯动态线性模型（ABCO），同时估计变点和局部异常值过程，适用于复杂序列中未指定变点的检测，对异常值和异方差噪声具有稳健性，并在美国经济数据中验证了效果。
We adaptively estimate both changepoints and local outlier processes in a Bayesian dynamic linear model with global-local shrinkage priors in a novel model we call Adaptive Bayesian Changepoints with Outliers (ABCO). We use a state-space approach to identify a dynamic signal in the presence of outliers and measurement error with stochastic volatility. We find that global state equation parameters are inadequate for most real applications and we include local parameters to track noise at each time-step. This setup provides a flexible framework to detect unspecified changepoints in complex series, such as those with large interruptions in local trends, with robustness to outliers and heteroscedastic noise. Finally, we compare our algorithm against several alternatives to demonstrate its efficacy in diverse simulation scenarios and two empirical examples on the U.S. economy.