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SVD-Bootstrap for Detection of Tensor Changes

In: Asymptotic and Methodological Statistics

Author

Listed:
  • Barbora Peštová

    (Charles University, Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics)

  • Michal Pešta

    (Charles University, Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics)

  • Martin Romaňák

    (Charles University, Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics)

Abstract

Multivariate observations over the items and across the subjects with longitudinal and cross-sectional dependence naturally form a stochastic tensor data structure. Several types of changes in tensor means are considered. A class of changepoint detection methods is investigated. These procedures do not require training data and, moreover, are completely distribution-free and tuning-parameter-free. We propose SVD-bootstrap superstructure that overcomes the computational curse of dimensionality without any loss of information. The empirical properties of the detection technique are investigated through a simulation study. The fully data-driven test is applied to real-world data from EEG.

Suggested Citation

  • Barbora Peštová & Michal Pešta & Martin Romaňák, 2026. "SVD-Bootstrap for Detection of Tensor Changes," Springer Books, in: Daniel Hlubinka & Šárka Hudecová & Matúš Maciak & Michal Pešta (ed.), Asymptotic and Methodological Statistics, chapter 0, pages 173-196, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-07178-1_9
    DOI: 10.1007/978-3-032-07178-1_9
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