Uncertainty evaluation for spatial prediction of curves remains an open issue in the functional data literature. We consider three different approaches that rely on semi-parametric bootstrapping, principal component analysis and classical inference for additive models respectively.
(2014). Kriging uncertainty for functional data: a comparison study [conference presentation - intervento a convegno]. Retrieved from http://hdl.handle.net/10446/31709
Kriging uncertainty for functional data: a comparison study
2014-01-01
Abstract
Uncertainty evaluation for spatial prediction of curves remains an open issue in the functional data literature. We consider three different approaches that rely on semi-parametric bootstrapping, principal component analysis and classical inference for additive models respectively.File allegato/i alla scheda:
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