New Modeling Approaches Based on Varimax Rotation of Functional Principal Components Acal González, Christian José Aguilera Del Pino, Ana María Escabias Machuca, Manuel Functional data analysis Functional principal components Varimax rotation B-splines COVID-19 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. 2021-01-28T08:28:31Z 2021-01-28T08:28:31Z 2020-11-22 info:eu-repo/semantics/article Acal, C., Aguilera, A. M., & Escabias, M. (2020). New Modeling Approaches Based on Varimax Rotation of Functional Principal Components. Mathematics, 8(11), 2085. [doi:10.3390/math8112085] http://hdl.handle.net/10481/66088 10.3390/math8112085 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España Mdpi