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.

Chiodi, M., Adelfio, G. (2011). Forward likelihood-based predictive approach for space–time point processes. ENVIRONMETRICS, 22(6), 749-757 [10.1002/env.1121].

Forward likelihood-based predictive approach for space–time point processes

CHIODI, Marcello;ADELFIO, Giada
2011-01-01

Abstract

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.
Settore SECS-S/01 - Statistica
Chiodi, M., Adelfio, G. (2011). Forward likelihood-based predictive approach for space–time point processes. ENVIRONMETRICS, 22(6), 749-757 [10.1002/env.1121].
File in questo prodotto:
File Dimensione Formato  
961_ftp.pdf

Solo gestori archvio

Dimensione 353.14 kB
Formato Adobe PDF
353.14 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/63465
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 17
  • ???jsp.display-item.citation.isi??? 18
social impact