B0184
Title: Dimension-agnostic change point testing
Authors: Xiaofeng Shao - University of Illinois at Urbana-Champaign (United States) [presenting]
Hanjia Gao - University of Illinois at Urbana-Champaign (United States)
Runmin Wang - Texas A&M University (United States)
Abstract: The detection 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. The test statistics can accommodate both temporal and cross-sectional dependence, regardless of the dimensionality. Both asymptotic theory and numerical studies confirm the appealing property of the proposed test.
