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Title: A general framework for constructing locally self-normalized multiple-change-point tests Authors:  Cheuk Hin Cheng - The Chinese University of Hong Kong (Hong Kong)
Kin Wai Chan - The Chinese University of Hong Kong (Hong Kong) [presenting]
Abstract: A general framework is proposed to construct self-normalized multiple-change- point tests with time series data. The only building block is a user-specified one-change-point detecting statistic, which covers a wide class of popular methods, 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. Case studies of NASDAQ option volume and Shanghai-Hong Kong Stock Connect turnover are provided.