A0539
Title: A general framework for constructing locally self-normalized multiple-change-point tests
Authors: Kin Wai Chan - The Chinese University of Hong Kong (Hong Kong) [presenting]
Cheuk Hin Cheng - The Chinese University of Hong Kong (Hong Kong)
Abstract: A general framework is proposed for constructing self-normalized multiple-change-point tests with time-series data. The framework is applicable to a wide class of popular change point detection statistics, including cumulative sum process, outlier-robust rank statistics and order statistics. Neither robust and consistent estimation of nuisance parameters, selection of bandwidth parameters, nor pre-specification of the number of change points is required. The finite-sample performance shows that our proposal is size-accurate, robust against misspecification of the alternative hypothesis, and more powerful than existing methods.
