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pro vyhledávání: '"de la Fuente, Manuel Oviedo"'
This paper proposes a new nonlinear approach for additive functional regression with functional response based on kernel methods along with some slight reformulation and implementation of the linear regression and the spectral additive model. The lat
Externí odkaz:
http://arxiv.org/abs/2207.04773
This paper considers the problem of variable selection in regression models in the case of functional variables that may be mixed with other type of variables (scalar, multivariate, directional, etc.). Our proposal begins with a simple null model and
Externí odkaz:
http://arxiv.org/abs/1801.00736
In this manuscript, we use meteorological information in Galicia (Spain) to propose a novel approach to predict the incidence of influenza. Our approach extends the GLS methods in the multivariate framework to functional regression models with depend
Externí odkaz:
http://arxiv.org/abs/1610.08718
Publikováno v:
Cuesta-Albertos, J. A., Febrero-Bande, M., & de la Fuente, M. O. (2017). The\hbox {DD}^ G-classifier in the functional setting. Test, 26(1), 119-142
The Maximum Depth was the first attempt to use data depths instead of multivariate raw data to construct a classification rule. Recently, the DD-classifier has solved several serious limitations of the Maximum Depth classifier but some issues still r
Externí odkaz:
http://arxiv.org/abs/1501.00372
Akademický článek
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Autor:
Azarang, Leyla1 lazarang@bcamath.org, de la Fuente, Manuel Oviedo2 manuel.oviedo@usc.es
Publikováno v:
R Journal. Dec2018, Vol. 10 Issue 2, p317-325. 9p.
