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Journal articles
A robust spatial autoregressive scalar-on-function regression with t-distribution
Abstract : Modelling functional data in the presence of spatial dependence is of great practical
importance as exemplified by applications in the fields of demography, economy
and geography, and has received much attention recently. However, for the classical
scalar-on-function regression (SoFR) with functional covariates and scalar responses,
only a relatively few literature is dedicated to this relevant area, which merits further
research. We propose a robust spatial autoregressive scalar-on-function regression by
incorporating a spatial autoregressive parameter and a spatial weight matrix into the
SoFR to accommodate spatial dependencies among individuals. The t-distribution
assumption for the error terms makes our model more robust than the classical spatial
autoregressive models under normal distributions. We estimate the model by firstly
projecting the functional predictor onto a functional space spanned by an orthonormal
functional basis and then presenting an expectation–maximization algorithm. Simulation
studies show that our estimators are efficient, and are superior in the scenario with
spatial correlation and heavy tailed error terms. A real weather dataset demonstrates
the superiority of our model to the SoFR in the case of spatial dependence.
https://hal-cnam.archives-ouvertes.fr/hal-02495912
Contributor : Gilbert Saporta Connect in order to contact the contributor
Submitted on : Monday, March 2, 2020 - 4:06:39 PM
Last modification on : Wednesday, October 13, 2021 - 7:16:03 PM
Contributor : Gilbert Saporta Connect in order to contact the contributor
Submitted on : Monday, March 2, 2020 - 4:06:39 PM
Last modification on : Wednesday, October 13, 2021 - 7:16:03 PM