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Functional factor analysis for periodic remote sensing data. (English) Zbl 1243.62083

Summary: We present a new approach to factor rotation for functional data. This is achieved by rotating the functional principal components toward a predefined space of periodic functions designed to decompose the total variation into components that are nearly periodic and nearly aperiodic with a predefined period. We show that the factor rotation can be obtained by calculations of canonical correlations between appropriate spaces which make the methodology computationally efficient. Moreover, we demonstrate that our proposed rotations provide stable and interpretable results in the presence of highly complex covariance. This work is motivated by the goal of finding interpretable sources of variability in gridded time series of vegetation index measurements obtained from remote sensing, and we demonstrate our methodology through an application of factor rotation of this data.

MSC:

62H25 Factor analysis and principal components; correspondence analysis
62H20 Measures of association (correlation, canonical correlation, etc.)
62P12 Applications of statistics to environmental and related topics
65C60 Computational problems in statistics (MSC2010)

Software:

fda (R); R
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Full Text: DOI arXiv Euclid

References:

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