Multipartition model for multiple change point identification
用于多重变化点识别的多分割模型AI 解读 ↓
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用于多重变化点识别的多分割模型
- 研究问题
- 当观测模型中的不同参数在不同时间发生变化时，如何同时识别变化点数量、位置以及究竟哪些参数发生了变化？
- 方法与思路
- 作者为每个参数设置独立于其他参数分割定义的时间轴随机分割，构建多分割模型，并用部分折叠 Gibbs 采样进行后验模拟。
- 材料陈述的贡献
- 摘要称模型克服传统共同分割难以辨认变化参数的限制，并在正态均值和方差变化的模拟与数据示例中表现具有竞争力。
- 阅读关注点
- 应核对各参数分割之间是否允许依赖、先验与变化点识别条件，以及部分折叠采样步骤、计算收敛和比较设计。
- 阅读边界
- 这是 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未说明先验、最小段长或具体数值，无法判断普遍性能优势。
原文摘要
Among the main goals in multiple change point problems are the estimation of the number and positions of the change points, as well as the regime structure in the clusters induced by those changes. The product partition model (PPM) is a widely used approach for the detection of multiple change points. The traditional PPM assumes that change points split the set of time points in random clusters that define a partition of the time axis. It is then typically assumed that sampling model parameter values within each of these blocks are identical. Because changes in different parameters of the observational model may occur at different times, the PPM thus fails to identify the parameters that experienced those changes. A similar problem may occur when detecting changes in multivariate time series. To solve this important limitation, we introduce a multipartition model to detect multiple change points occurring in several parameters at possibly different times. The proposed model assumes that the changes experienced by each parameter generate a different random partition of the time axis, which facilitates identifying which parameters have changed and when they do so. We discuss a partially collapsed Gibbs sampler scheme to implement posterior simulation under the proposed model. We apply the proposed model to identify multiple change points in Normal means and variances and evaluate the performance of the proposed model through Monte Carlo simulations and data illustrations. Its performance is compared with some previously proposed approaches for change point problems. These studies show that the proposed model is competitive and enriches the analysis of change point problems.