趋势与方差自适应贝叶斯变点分析及局部离群值评分
Trend and Variance Adaptive Bayesian Changepoint Analysis & Local Outlier Scoring
AI总结：
本文提出ABCO模型，结合全局-局部收缩先验和状态空间方法，在贝叶斯动态线性模型中自适应估计变点与局部离群值，并通过局部参数跟踪噪声，实现对复杂序列变点的稳健检测，模拟及美国经济实证验证了其有效性。
摘要：
我们在一种名为自适应贝叶斯变点与离群值（ABCO）的新模型中，利用全局-局部收缩先验，在贝叶斯动态线性模型中自适应地估计变点和局部离群值过程。我们采用状态空间方法，在存在离群值和具有随机波动性的测量误差的情况下识别动态信号。我们发现，全局状态方程参数对于大多数实际应用而言并不充分，因此我们引入局部参数来跟踪每个时间步的噪声。这一设置提供了一个灵活的框架，用于检测复杂序列中未指定的变点（例如局部趋势出现大幅中断的序列），并对离群值和异方差噪声具有稳健性。最后，我们将我们的算法与几种替代方法进行比较，以证明其在多种模拟场景以及两个关于美国经济的实证示例中的有效性。
英文摘要：
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 utilize 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 heteroskedastic 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
