Functional factor analysis for periodic remote sensing data

Liu, C., Ray, S. , Hooker, G. and Friedl, M. (2012) Functional factor analysis for periodic remote sensing data. Annals of Applied Statistics, 6(2), pp. 601-624. (doi: 10.1214/11-AOAS518)

Abstract

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ray, Professor Surajit
• Authors: Liu, C., Ray, S., Hooker, G., and Friedl, M.
• Subjects: H Social Sciences > HA Statistics
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Annals of Applied Statistics
• Publisher: Institute of Mathematical Statistics
• ISSN: 1932-6157
• Copyright Holders: Copyright © 2012 Institute of Mathematical Statistics
• First Published: First published in Annals of Applied Statistics 6(2):601-624
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher