TITLE: Changepoint detection for locally dependent data
Abstract: Local dependence is common in multivariate and object data sequences. We consider the testing and estimation of changepoints in such sequences. A new way of permutation, circular block permutation with a randomized starting point, is proposed and studied for a scan statistic utilizing graphs representing the similarity between observations. The proposed permutation approach could correctly address for local dependence and make it possible the theoretical treatments for the nonparametric graphbased scan statistic for locally dependent data. We derive accurate analytic approximations to the significance of graphbased scan statistics under the circular block permutation framework, facilitating its application to locally dependent multivariate or object data sequences.
Short Bio: Hao Chen is an Assistant Professor in the department of Statistics at UC Davis. She obtained her PhD in Statistics from Stanford University and a Bachelor's degree in Biology from Tsinghua University.
