Local linear regression for functional data
By Alain F. Berlinet, Abdallah Elamine, André Mas. 2007
65 citations recorded by the source corpus.
We study a non-linear regression model with functional data as inputs and scalar response. We propose a pointwise estimate of the regression function that maps a Hilbert space onto the real line by a local linear method and derive its asymptotic mean square error. Computations involve a linear inverse problem as well as a representation of the small ball probability of the data and are based on recent advances in this area.
Subfields: Applied Mathematics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/59e7ef01b28714935ef2d1275f1a65ea0330ea1c
How researchers see this paper
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In Applied Mathematics
In Statistics and Probability
- Nengxiang Ling
- Philippe Vieu
- Heng Lian
- Peter Hall
- Frédéric Ferraty
- M. Febrero–Bande
- H. Muller
- Antonio Cuevas
Citations in the corpus
- Nonparametric modelling for functional data: selected survey and tracks for future (2018, background): “[45] and [46], asymptotic expansions for L2 errors are given in [47,48] and limit distribution is also obtained in Zhao and Lin [48].”