Detection of Multiple Changes of Variance Using Posterior Odds
提出一种基于后验比值的贝叶斯方法，用于评估时间序列中是否存在多个方差变化点，适用于独立观测序列并扩展至自回归模型，通过后验分布总结变化点位置和方差大小，并用金融序列示例。
This article uses a Bayesian procedure based on obtaining posterior odds to assess the evidence about the existence of multiple changes of variance in a time series. The approach is developed for sequences of independent observations. An extension to consider autoregressive models is also discussed. The information on the data about the location of the change points and the magnitude of the variances at the different pieces of the series is summarized through posterior distributions. The procedure is illustrated with a well-known financial series.