题目：Optimal Spatial Anomaly Detection — Minimax Theory and Applications

时间：2026年4月28日（周二），上午10:00—11:00

地点：数学楼2-2会议室

讲座内容：There has been a growing interest in multiple changepoints/anomaly detection problems recently, whilst their focuses are mostly on changes taking place on the time index. In this work, we investigate the anomaly-in-mean model on multidimensional spatial lattice, that is, to detect the number and locations of anomaly spatial regions from the baseline. In addition to the usual minimisation over cost function with a penalisation related to the number of anomalies, we also introduce a new penalty on the area of minimum convex hull that covers the anomaly regions. A composite likelihood method is developed to accommodate spatial dependence arising from both short-range and long-range dependence regimes. We show that our estimation on the number and locations of anomalies are consistent, and prove that the method achieves optimal localisation error under the minimax framework. We also proposed a dynamic programming algorithm to solve the penalised cost minimisation approximately and carry out large-scale Monte Carlo simulations to examine its performance. The method has a wide range of applications in climate problem. As an example, we apply it to detect the marine heatwaves using the sea surface temperature data from European Space Agent.

报告人简介：Chao Zheng is a Lecturer (Assistant Professor) in the School of Mathematical Sciences at the University of Southampton. He received Ph.D in Statistics from the University of Melbourne (Australia) and was a postdoctoral research associate at Lancaster University in the UK. His research interests include changepoint detection, high dimensional data analysis, nonparametric statistis, robust estimation, and machine learning theory.

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