2025 The 17th CEA (Europe) & 36th CEA (UK) Annual Conference
Date:
This talk discusses Sequential Change Point Detection for Time Series – An Adjusted-Range Based Approach, presented at the 17th CEA (Europe) & 36th CEA (UK) Annual Conference.
Citation: Jiajing Sun. (2025). “Sequential Change Point Detection for Time Series – An Adjusted-Range Based Approach.” Presented at the 17th CEA (Europe) & 36th CEA (UK) Annual Conference, Glasgow, UK.
Abstract:
The ability to update models in real-time to reflect the evolving scope of real-world data is a fundamental task in statistics. Existing cumulative sum (CUSUM)-type procedures need to specify tuning parameters such as kernel, bandwidth, or block size in block bootstrap when estimating the long-run variance (LRV). The weak power of the KS-type statistic using the existing self-normalization (SN) method proposed by Shao (2010) limits its use in sequential change-point detection. This paper proposes a novel adjusted-range self-normalized sequential change-point monitoring scheme. We conduct asymptotic analysis under the null hypothesis and establish the consistency of the proposed sequential change-point scheme under weak regularity conditions. Through Monte Carlo simulations and empirical analysis, we find that our proposed method can sequentially detect structural changes in a timely fashion and is robust to “mild” misspecification in the training sample.