This article is published in Environmetrics as a special issue on Spatio-Temporal Stochastic Modelling (METMAV), edited by Wenceslao González-Manteiga and Rosa M. Crujeiras, University of Santiago de Compostela, Spain.
Special Issue Paper
Nonparametric methods for spatial regression. An application to seismic events†
Article first published online: 19 DEC 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Special Issue: Spatio-Temporal Stochastic Modelling. (METMAV)
Volume 23, Issue 1, pages 85–93, February 2012
How to Cite
Francisco-Fernández, M., Quintela-del-Río, A. and Fernández-Casal, R. (2012), Nonparametric methods for spatial regression. An application to seismic events. Environmetrics, 23: 85–93. doi: 10.1002/env.1146
- Issue published online: 16 JAN 2012
- Article first published online: 19 DEC 2011
- Manuscript Accepted: 14 NOV 2011
- Manuscript Revised: 24 OCT 2011
- Manuscript Received: 4 JAN 2011
- MEC. Grant Number: MTM2008-03010
- local polynomial regression;
Nonparametric regression estimation is a powerful tool to handle multidimensional data. When a dependent data set is analyzed, classical techniques need to be modified to provide useful results. In this work, different approximations to take the spatial dependence into account are exposed. A bandwidth selection technique that adjusts the generalized cross-validation criterion for the effect of spatial correlation, in the case of bivariate local polynomial regression, is considered. Moreover, a bootstrap algorithm is designed to assess the variability of the estimated spatial maps, and also to estimate the probability of obtaining a response variable larger than or equal to a given threshold, for a specific point. A simulation study checks the validity of the presented approaches in practice. The broad applicability of the procedures is demonstrated on a data set of earthquakes in the Iberian Peninsula. Copyright © 2011 John Wiley & Sons, Ltd.