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Activity Number: 425 - Nonparametric Methods for Dependent Data
Type: Contributed
Date/Time: Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #322881
Title: Dimension-Agnostic Change Point Testing and Estimation
Author(s): Hanjia Gao* and Runmin Wang and Xiaofeng Shao
Companies: University of Illinois at Urbana-Champaign and Southern Methodist University and University of Illinois at Urbana-Champaign
Keywords: Change-point testing; Self-normalization; Power analysis; Wild binary segmentation

The detection and estimation of change-point(s) in the mean is a classical problem in statistics and has broad applications in a wide range of areas. Though many methods have been developed in the literature, most are applicable only under a specific dimensional setting. Specifically, the methods designed for low-dimensional problems may not work well in the high-dimensional environment and vice versa. Motivated by this limitation, we propose a dimension-agnostic procedure of change-point testing for time series by applying dimension reduction and self-normalization. Our test statistics can accommodate both temporal and cross-sectional dependence, and capture both sparse and dense changes, regardless of the dimensionality. An extension to change point estimation is also made via the wild binary segmentation. These appealing features are supported by both asymptotic theory and numerical studies.

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

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