Open Access
June 2012 Functional factor analysis for periodic remote sensing data
Chong Liu, Surajit Ray, Giles Hooker, Mark Friedl
Ann. Appl. Stat. 6(2): 601-624 (June 2012). DOI: 10.1214/11-AOAS518


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 calculation 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.


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Chong Liu. Surajit Ray. Giles Hooker. Mark Friedl. "Functional factor analysis for periodic remote sensing data." Ann. Appl. Stat. 6 (2) 601 - 624, June 2012.


Published: June 2012
First available in Project Euclid: 11 June 2012

zbMATH: 1243.62083
MathSciNet: MR2976484
Digital Object Identifier: 10.1214/11-AOAS518

Keywords: covariance surface , Factor rotation , Functional data analysis , principal periodic components , remote sensing , variance decomposition

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.6 • No. 2 • June 2012
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