Novel whitening approaches in functional settings Vidal, Marc Aguilera Del Pino, Ana María Correlation operator Cross-covariance operator Functional independent component analysis Mahalanobis distance Sphering Whitening operator Whitening is a critical normalization method to enhance statistical reduction via reparametrization to unit covariance. This article introduces the notion of whitening for random functions assumed to reside in a real separable Hilbert space. We compare the properties of different whitening transformations stemming from the factorization of a bounded precision operator under a particular geometrical structure. The practical performance of the estimators is shown in a simulation study, providing helpful insights into their optimization. Computational algorithms for the estimation of the proposed whitening transformations in terms of basis expansions of a functional data set are also provided. 2023-02-10T13:05:29Z 2023-02-10T13:05:29Z 2022-10-19 info:eu-repo/semantics/article Vidal, M., & Aguilera, A. M. (2023). Novel whitening approaches in functional settings. Stat, 12( 1), e516. [https://doi.org/10.1002/sta4.516] https://hdl.handle.net/10481/79831 10.1002/sta4.516 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional Wiley