Publication: Local linear regression for functional predictor and scalar response
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2007-08
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Abstract
The aim of this work is to introduce a new nonparametric regression technique in the context of
functional covariate and scalar response. We propose a local linear regression estimator and study
its asymptotic behaviour. Its finite-sample performance is compared with a Nadayara-Watson type
kernel regression estimator via a Monte Carlo study and the analysis of two real data sets. In all
the scenarios considered, the local linear regression estimator performs better than the kernel one,
in the sense that the mean squared prediction error and its standard deviation are lower.
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Functional data, Nonparametric smoothing, Local linear regression, Kernel regression, Fourier expansion, Cross-validation