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Activity Number: 175 - Clustering and Changepoint Analysis
Type: Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: Korean International Statistical Society
Abstract #304493
Title: Inference for Change Points in High-Dimensional Data via Self-Normalization
Author(s): Runmin Wang*
Companies: University of Illinois at Urbana-Champaign
Keywords: CUSUM; Segmentation; U-Statistic; Self-normalization
Abstract:

This article considers change point testing and estimation for high dimensional data. In the case of testing for a mean shift, we propose a new test which is based on U-statistics and utilizes the self-normalization principle. Our test targets dense alternatives in the high dimensional setting and involves no tuning parameters. The weak convergence of a sequential U-statistic based process is shown as an important theoretical contribution. Extensions to testing for multiple unknown change points in the mean, and testing for changes in the covariance matrix are also presented with rigorous asymptotic theory and encouraging simulation results. Additionally, we illustrate how our approach can be used in combination with wild binary segmentation to estimate the number and location of multiple unknown change points.


Authors who are presenting talks have a * after their name.

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