Novel whitening approaches in functional settings

Whitening is a critical normalization method to enhance statistical reduction via reparametrization unit covariance. This article introduces the notion of whitening for random functions assumed reside in real separable Hilbert space. We compare properties different transformations stemming from factorization bounded precision operator under particular geometrical structure. The practical performance estimators shown simulation study, providing helpful insights into their optimization. Computational algorithms estimation proposed terms basis expansions functional data set are also provided.