论文标题
检测高维自激发泊松过程的突然变化
Detecting Abrupt Changes in High-Dimensional Self-Exciting Poisson Processes
论文作者
论文摘要
高维自我激发点过程已在许多应用领域广泛使用，以模拟过去和时事影响未来事件可能性的离散事件数据。在本文中，我们关注的是在离散的高维自激发泊松过程中检测到系数矩阵的突然变化，这在现有文献中尚待研究，这是由于理论和计算挑战均植根于基于基础过程的非平稳性和高维质的理论和计算挑战。我们提出了一种受惩罚的动态编程方法，该方法得到了理论率分析和数值证据的支持。
High-dimensional self-exciting point processes have been widely used in many application areas to model discrete event data in which past and current events affect the likelihood of future events. In this paper, we are concerned with detecting abrupt changes of the coefficient matrices in discrete-time high-dimensional self-exciting Poisson processes, which have yet to be studied in the existing literature due to both theoretical and computational challenges rooted in the non-stationary and high-dimensional nature of the underlying process. We propose a penalized dynamic programming approach which is supported by a theoretical rate analysis and numerical evidence.
