PREVIOUS SEMINAR - 7th May 2021
Speaker: Lynna Chu (Iowa State University)
Title: Sequential Change-point Detection for High-Dimensional and non-Euclidean Data
Abstract: In many modern applications, high-dimensional/non-Euclidean data sequences are collected to study complex phenomena over time and it is often of scientific significance to detect anomaly events as data is continually being collected. We study a nonparametric framework that utilizes nearest neighbor information among the observations and can be applied to various data types to detect changes in an online setting. We consider new test statistics under this framework that can detect anomaly events more effectively than the existing test with the false discovery rate controlled at the same level. Analytical formulas to determine the threshold of claiming a change are also provided, making the approach easily applicable for real data applications.