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Change point analysis of functional variance function with stationary error

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  • Hu, Qirui

Abstract

An asymptotically correct test for an abrupt break in functional variance function of measurement error in the functional sequence and the confidence interval of change point is constructed. Under general assumptions, the test and detection procedure conducted by Spline-backfitted kernel smoothing, i.e., recovering trajectories with B-spline and estimating variance function with kernel regression, enjoy oracle efficiency, namely, the proposed procedure is asymptotically indistinguishable from that with accurate trajectories. Furthermore, a consistent algorithm for multiple change points based on the binary segment is derived. Extensive simulation studies reveal a positive confirmation of the asymptotic theory. The proposed method is applied to analyze EEG data.

Suggested Citation

  • Hu, Qirui, 2024. "Change point analysis of functional variance function with stationary error," Journal of Multivariate Analysis, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:jmvana:v:202:y:2024:i:c:s0047259x24000186
    DOI: 10.1016/j.jmva.2024.105311
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    References listed on IDEAS

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    1. Zhenguo Gao & Zuofeng Shang & Pang Du & John L. Robertson, 2019. "Variance Change Point Detection Under a Smoothly-Changing Mean Trend with Application to Liver Procurement," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 773-781, April.
    2. Rice, Gregory & Zhang, Chi, 2022. "Consistency of binary segmentation for multiple change-point estimation with functional data," Statistics & Probability Letters, Elsevier, vol. 180(C).
    3. Horváth, Lajos & Rice, Gregory & Zhao, Yuqian, 2022. "Change point analysis of covariance functions: A weighted cumulative sum approach," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    4. Michael Messer, 2022. "Bivariate change point detection: Joint detection of changes in expectation and variance," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 886-916, June.
    5. Raymond J. Carroll, 2003. "Variances Are Not Always Nuisance Parameters," Biometrics, The International Biometric Society, vol. 59(2), pages 211-220, June.
    6. Alexander Aue & Gregory Rice & Ozan Sönmez, 2018. "Detecting and dating structural breaks in functional data without dimension reduction," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(3), pages 509-529, June.
    7. Cai, Leheng & Hu, Qirui, 2024. "Simultaneous inference and uniform test for eigensystems of functional data," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
    8. Hansen, Bruce E., 2005. "Exact Mean Integrated Squared Error Of Higher Order Kernel Estimators," Econometric Theory, Cambridge University Press, vol. 21(6), pages 1031-1057, December.
    9. Fan, Jianqing & Yao, Qiwei, 1998. "Efficient estimation of conditional variance functions in stochastic regression," LSE Research Online Documents on Economics 6635, London School of Economics and Political Science, LSE Library.
    10. Guanqun Cao & Lijian Yang & David Todem, 2012. "Simultaneous inference for the mean function based on dense functional data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(2), pages 359-377.
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