PREVIOUS SEMINAR - 5th June 2020
Speaker: Yudong Chen (University of Cambridge)
Title: High-dimensional, multiscale online changepoint detection
Abstract: We introduce a new method for high-dimensional, online changepoint detection in settings where a p-variate Gaussian data stream may undergo a change in mean. The procedure works by performing likelihood ratio tests against simple alternatives of dif- ferent scales in each coordinate, and then aggregating test statistics across scales and coordinates. The algorithm is online in the sense that its worst-case computational complexity per new observation, namely O(p^2 log(ep)), is independent of the number of previous observations; in practice, it may even be significantly faster than this. We prove that the patience, or average run length under the null, of our procedure is at least at the desired nominal level, and provide guarantees on its response delay under the alternative that depend on the sparsity of the vector of mean change. Simulations confirm the practical effectiveness of our proposal. This talk is based on joint work with Tengyao Wang and Richard Samworth.