Abstract
In this work, we have focused on the nonparametric regression model with scalar response and functional covariate, and we have analyzed the existence of underlying complex structures in data by means of a thresholding procedure. Several thresholding functions are proposed, and a cross-validation criterion is used in order to estimate the threshold value. Furthermore, a simulation study shows the effectiveness of our method.
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References
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© 2011 Springer-Verlag Berlin Heidelberg
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Ferraty, F., Martínez-Calvo, A., Vieu, P. (2011). Thresholding in Nonparametric Functional Regression with Scalar Response. In: Ferraty, F. (eds) Recent Advances in Functional Data Analysis and Related Topics. Contributions to Statistics. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2736-1_16
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DOI: https://doi.org/10.1007/978-3-7908-2736-1_16
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Publisher Name: Physica-Verlag HD
Print ISBN: 978-3-7908-2735-4
Online ISBN: 978-3-7908-2736-1
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