Abstract
Change point detection in sequences of functional data is examined where the functional observations are dependent. The theoretical properties for tests for at most one change are derived with a special focus on power analysis. It is shown that the usual desirable properties of PCA to represent large amounts of the variation in a few components can actually be detrimental in the case of change point detection.
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References
Berkes, I., Gabrys, R., Horvath, L., Kokoszka, P.: Detecting changes in the mean of functional observations. J. R. Stat. Soc. Ser. B 71, 927–946 (2009)
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© 2011 Springer-Verlag Berlin Heidelberg
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Aston, J.A.D., Kirch, C. (2011). Power Analysis for Functional Change Point Detection. In: Ferraty, F. (eds) Recent Advances in Functional Data Analysis and Related Topics. Contributions to Statistics. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2736-1_4
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DOI: https://doi.org/10.1007/978-3-7908-2736-1_4
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Publisher Name: Physica-Verlag HD
Print ISBN: 978-3-7908-2735-4
Online ISBN: 978-3-7908-2736-1
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