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

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9 Citazioni (Scopus)

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.
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

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title = "Forward likelihood-based predictive approach for space–time point processes",
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.",
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AU - Chiodi, Marcello

AU - Adelfio, Giada

PY - 2011

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AB - 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.

KW - likelihood function; nonparametric estimation; predictive properties; space–time point processes

UR - http://hdl.handle.net/10447/63465

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SP - 749

EP - 757

JO - Environmetrics

JF - Environmetrics

SN - 1180-4009

ER -