Abstract
Principal Components Analysis is a well-known method for reduction of dimension in Data Analysis. Considering a cyclostationary random function, we use appropriate transformations, based on spectral properties, in order to get a stationary random function, and then to process to a principal components analysis in the frequency domain. Then, a cyclostationary function is reconstituted as a summary of the initial cyclostationary function. Applications on simulated data illustrate the method.
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Boudou, A., Viguier-Pla, S. (2020). Principal Components Analysis of a Cyclostationary Random Function. In: Aneiros, G., Horová, I., Hušková, M., Vieu, P. (eds) Functional and High-Dimensional Statistics and Related Fields. IWFOS 2020. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-47756-1_6
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DOI: https://doi.org/10.1007/978-3-030-47756-1_6
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