These articles are published in Environmetrics as a special issue on Handling complexity and uncertainty in environmental studies, arising from the TIES- GRASPA joint conference held in Bologna in 2009 and is edited by Daniela Cocchi, Department of Statistics University of Bologna, Italy and E. Marian Scott, School of Mathematics and Statistics, University of Glasgow, UK.
Special Issue Paper
Forward likelihood-based predictive approach for space–time point processes†
Version of Record online: 2 MAY 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Special Issue: Handling Complexity and Uncertainty in Environmental Studies, Arising from the TIES-GRASPA Joint Conference Held in Bologna in 2009
Volume 22, Issue 6, pages 749–757, September 2011
How to Cite
Chiodi, M. and Adelfio, G. (2011), Forward likelihood-based predictive approach for space–time point processes. Environmetrics, 22: 749–757. doi: 10.1002/env.1121
- Issue online: 9 SEP 2011
- Version of Record online: 2 MAY 2011
- Manuscript Revised: 29 MAR 2011
- Manuscript Accepted: 29 MAR 2011
- Manuscript Received: 30 NOV 2009
- likelihood function;
- nonparametric estimation;
- predictive properties;
- space–time point processes
Dealing with data from a space–time point process, the estimation of the conditional intensity function is a crucial issue even if a complete definition of a parametric model is not available. In particular, in case of exploratory contexts or if we want to assess the adequacy of a specific parametric model, some kind of nonparametric estimation procedure could be useful.
Often, for these purposes kernel estimators are used and the estimation of the intensity function depends on the estimation of bandwidth parameters. In some fields, like for instance the seismological one, predictive properties of the estimated intensity function are pursued. Since a direct ML approach cannot be used, we propose an estimation procedure based on the subsequent increments of likelihood obtained adding an observation one at a time. Simulated results and some applications to statistical seismology are provided. Copyright © 2011 John Wiley & Sons, Ltd.