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Change point analysis of functional variance function with stationary error

Author

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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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