Functional linear regression with derivatives

André Mas and Besnik Pumo

Journal of Nonparametric Statistics, 2009, vol. 21, issue 1, 19-40

Abstract: We introduce a new model of linear regression for random functional inputs taking into account the first-order derivative of the data. We propose an estimation method that comes down to solving a special linear inverse problem. Our procedure tackles the problem through a double and synchronised penalisation. An asymptotic expansion of the mean square prevision error is given. The model and the method are applied to a benchmark dataset of spectrometric curves and compared with other functional models.

Date: 2009
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DOI: 10.1080/10485250802401046

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