Wavelets in Functional Data Analysis: estimation of multidimensional curves and their derivatives
Keywords
Statistical learning
Code:
09/2011
Title:
Wavelets in Functional Data Analysis: estimation of multidimensional curves and their derivatives
Date:
Friday 18th February 2011
Author(s):
Pigoli, D.; Sangalli, L.
Download link:
Abstract:
A wavelet-based method is proposed to obtain accurate estimates of curves in more than one dimension and of their derivatives. By means
of simulation studies, we compare this novel method to another locally-adaptive estimation technique for multidimensional functional data, based on free-knot regression splines. This comparison shows that the proposed method is particularly attractive when the curves to be estimated present strongly localized features. The multidimensional wavelet estimation method is thus applied to multi-lead electrocardiogram records, where strongly localized features are indeed expected.
This report, or a modified version of it, has been also submitted to, or published on
Davide Pigoli and Laura M. Sangalli (2012), Wavelets in Functional Data Analysis: estimation of multidimensional curves and their derivatives, Computational Statistics and Data Analysis, 56, 1482-1498.
Davide Pigoli and Laura M. Sangalli (2012), Wavelets in Functional Data Analysis: estimation of multidimensional curves and their derivatives, Computational Statistics and Data Analysis, 56, 1482-1498.