Statistics and Data Science Seminar
Lynna Chu
Iowa State University
Graph-based change-point detection for non-Euclidean and multivariate data
Abstract: We present a new framework for the testing and estimation of change-points, locations where the distribution abruptly changes. While the change-point problem has been extensively studied for low-dimensional data, advances in data collection technology have produced data sequences of increasing volume and complexity. Motivated by the challenges of modern data, we study a non-parametric framework that utilizes similarity information among observations and can be applied to various data types as long as an informative similarity measure on the sample space can be defined. Analytical p-value approximations are also provided, making the methods easy-off-the-shelf tools for real applications.
Wednesday October 14, 2020 at 4:00 PM in Zoom