@misc{10481/79831,
year = {2022},
month = {10},
url = {https://hdl.handle.net/10481/79831},
abstract = {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.},
organization = {Ministry of Science and Innovation, Spain (MICINN)
Instituto de Salud Carlos III
Spanish Government	PID2020-113961GB-I00},
organization = {Methusalem, Vlaamse regering},
publisher = {Wiley},
keywords = {Correlation operator},
keywords = {Cross-covariance operator},
keywords = {Functional independent component analysis},
keywords = {Mahalanobis distance},
keywords = {Sphering},
keywords = {Whitening operator},
title = {Novel whitening approaches in functional settings},
doi = {10.1002/sta4.516},
author = {Vidal, Marc and Aguilera Del Pino, Ana María},
}