Forward likelihood-based predictive approachfor space–time point processes

Marcello Chiodi, Giada Adelfio

Risultato della ricerca: Article

10 Citazioni (Scopus)

Abstract

Dealing with data from a space–time point process, the estimation of the conditional intensity function is a crucial issue evenif a complete definition of a parametric model is not available. In particular, in case of exploratory contexts or if we want toassess 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 theestimation of bandwidth parameters. In some fields, like for instance the seismological one, predictive properties ofthe estimated intensity function are pursued. Since a direct ML approach cannot be used, we propose an estimationprocedure based on the subsequent increments of likelihood obtained adding an observation one at a time. Simulatedresults and some applications to statistical seismology are provided. Copyright 2011 John Wiley & Sons, Ltd.
Lingua originaleEnglish
pagine (da-a)749-757
Numero di pagine0
RivistaEnvironmetrics
Volume22
Stato di pubblicazionePublished - 2011

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Intensity Function
Point Process
Likelihood
Space-time
Parametric Model
seismology
Seismology
Kernel Estimator
Nonparametric Estimation
Increment
Bandwidth
parameter

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Ecological Modelling

Cita questo

Forward likelihood-based predictive approachfor space–time point processes. / Chiodi, Marcello; Adelfio, Giada.

In: Environmetrics, Vol. 22, 2011, pag. 749-757.

Risultato della ricerca: Article

Chiodi, M & Adelfio, G 2011, 'Forward likelihood-based predictive approachfor space–time point processes', Environmetrics, vol. 22, pagg. 749-757.
Chiodi M, Adelfio G. Forward likelihood-based predictive approachfor space–time point processes. Environmetrics. 2011;22:749-757.
Chiodi, Marcello ; Adelfio, Giada. / Forward likelihood-based predictive approachfor space–time point processes. In: Environmetrics. 2011 ; Vol. 22. pagg. 749-757.
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