@misc{10481/66088,
year = {2020},
month = {11},
url = {http://hdl.handle.net/10481/66088},
abstract = {Functional Principal Component Analysis (FPCA) is an important dimension reduction
technique to interpret themainmodes of functional data variation in terms of a small set of uncorrelated
variables. The principal components can not always be simply interpreted and rotation is one of the main
solutions to improve the interpretation. In this paper, two new functional Varimax rotation approaches
are introduced. They are based on the equivalence between FPCA of basis expansion of the sample
curves and Principal Component Analysis (PCA) of a transformation of thematrix of basis coefficients.
The first approach consists of a rotation of the eigenvectors that preserves the orthogonality between the
eigenfunctions but the rotated principal component scores are not uncorrelated. The second approach is
based on rotation of the loadings of the standardized principal component scores that provides uncorrelated
rotated scores but non-orthogonal eigenfunctions. A simulation study and an application with data from
the curves of infections by COVID-19 pandemic in Spain are developed to study the performance of these
methods by comparing the results with other existing approaches.},
organization = {Spanish Ministry of Science, Innovation and Universities (FEDER program) 	
MTM2017-88708-P},
organization = {Government of Andalusia (Spain) 	
FQM-307
 	
FPU18/01779},
publisher = {Mdpi},
keywords = {Functional data analysis},
keywords = {Functional principal components},
keywords = {Varimax rotation},
keywords = {B-splines},
keywords = {COVID-19},
title = {New Modeling Approaches Based on Varimax Rotation of Functional Principal Components},
doi = {10.3390/math8112085},
author = {Acal González, Christian José and Aguilera Del Pino, Ana María and Escabias Machuca, Manuel},
}