Testing Stability in Functional Event Observations with an Application to IPO Performance
事件时点观测的函数型数据稳定性检验及 IPO 表现应用AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究随时间出现的函数型观测及事件频率、均值的变点，直接对应结构变化和时间序列。
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事件时点观测的函数型数据稳定性检验及 IPO 表现应用
- 研究问题
- 当函数型观测仅在事件发生时出现且事件频率随时间变化时，如何区分观测机制变化与函数均值变化，并定位变点？
- 方法与思路
- 作者先用新的二元分割程序识别事件频率近似同质的区段，按频率调整曲线后，再检验和估计函数均值的变点。
- 材料陈述的贡献
- 摘要称建立两阶段变点检测与估计的一致性和渐近分布，并通过蒙特卡洛及 IPO 后股价轨迹数据展示方法。
- 阅读关注点
- 应核对事件频率与曲线分布之间的关系、频率调整方式，以及第一阶段分割误差如何影响第二阶段的检验与定位。
- 阅读边界
- 仅据所给 OpenAlex 版本摘要，无法判断两类变化能否在任意事件机制下区分；理论条件与应用结果未说明，未核对刊本变化。
原文摘要
Many sequentially observed functional data objects are available only at the times of certain events. For example, the trajectory of stock prices of companies after their initial public offering (IPO) can be observed when the offering occurs, and the resulting data may be affected by changing circumstances. It is of interest to investigate whether the mean behavior of such functions is stable over time, and if not, to estimate the times at which apparent changes occur. Since the frequency of events may fluctuates over time, we propose a change point analysis that has two steps. In the first step, we segment the series into segments in which the frequency of events is approximately homogeneous using a new binary segmentation procedure for event frequencies. After adjusting the observed curves in each segment based on the frequency of events, we proceed in the second step by developing a method to test for and estimate change points in the mean of the observed functional data objects. We establish the consistency and asymptotic distribution of the change point detector and estimator in both steps, and study their performance using Monte Carlo simulations. An application to IPO performance data illustrates the proposed methods.