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A self-normalized approach to sequential change-point detection for time series

Ngai Hang Chan*, Wai Leong Ng, Chun Yip Yau

*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

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Abstract

We propose a self-normalization sequential change-point detection method for time series. To test for parameter changes, most traditional sequential monitoring tests use a cumulative sum-based test statistic, which involves a long-run variance estimator. However, such estimators require choosing a bandwidth parameter, which may be sensitive to the performance of the test. Moreover, traditional tests usually suffer from severe size distortion as a result of the slow convergence rate to the limit distribution in the early monitoring stage. We propose self-normalization method to address these issues. We establish the null asymptotic and the consistency of the proposed sequential change-point test under general regularity conditions. Simulation experiments and an applications to railway-bearing temperature data illustrate and verify the proposed method.
Original languageEnglish
Pages (from-to)491-517
JournalStatistica Sinica
Volume31
Issue number1
DOIs
Publication statusPublished - Jan 2021
Externally publishedYes

Research Keywords

  • ARMA-GARCH model
  • On-line detection
  • Pairwise likeli-hood
  • Quickest detection
  • Sequential monitoring
  • Stochastic volatility model

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    Project: Research

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