Materiales
Abstract
Functional linear models are one of the most fundamental tools to assess the relation between two random variables of a functional or scalar nature. This contribution proposes a goodness-of-fit test for the functional linear model with functional response that neatly adapts to functional/scalar responses/predictors. In particular, the new goodness-of-fit test extends a previous proposal for scalar response. The test statistic is based on a convenient regularized estimator, is easy to compute, and is calibrated through an efficient bootstrap resampling. A graphical diagnostic tool, useful to visualize the deviations from the model, is introduced and illustrated with a novel data application. The R package goffda implements the proposed methods and allows for the reproducibility of the data application.
Código R y datos
El código está documentado y libremente disponible en el repositorio GitHub. Además dicho software fue subido como paquete de R.
Hay dos archivos de datos incluidos en el repositorio:
data/aemet_temp.rda: fichero de curvas de temperatura del AEMET de 1974 a 2013.data/ontario.rda: fichero de curvas de demanda eléctrica
Cita BibTeX
@book{GarciaPortuguesetal2020,
author = {E. García-Portugués and J. Álvarez-Liébana and G. Álvarez-Pérez and W. González-Manteiga},
title = {Goodness-of-fit Tests for Functional Linear Models Based on Integrated Projections},
publisher = {In: Aneiros, G., Horová, I., Hušková, M., Vieu, P. (eds) Functional and High-Dimensional Statistics and Related Fields. IWFOS 2020. Contributions to Statistics. Springer, Cham},
doi = {10.1007/978-3-030-47756-1_15},
url = {https://link.springer.com/chapter/10.1007/978-3-030-47756-1_15},
year = {2020}
}