A0730
Title: Asymptotic distribution-free change-point detection for modern data based on a new ranking scheme
Authors: Doudou Zhou - National University of Singapore (Singapore) [presenting]
Hao Chen - University of California at Davis (United States)
Abstract: Change-point detection (CPD) involves identifying distributional changes in a sequence of independent observations. Among nonparametric methods, rank-based methods are attractive due to their robustness and effectiveness and have been extensively studied for univariate data. However, they are not well explored for high-dimensional or non-Euclidean data. A new method, rank induced by graph change-point detection (RING-CPD), is proposed, which utilizes graph-induced ranks to handle high-dimensional and non-Euclidean data. The new method is asymptotically distribution-free under the null hypothesis, and an analytic $p$-value approximation is provided for easy type-I error control. Simulation studies show that RING-CPD effectively detects change points across a wide range of alternatives and is also robust to heavy-tailed distribution and outliers. The new method is illustrated by the detection of seizures in a functional connectivity network dataset, changes in digit images, and travel pattern changes in the New York City Taxi dataset.
