Space-time Point Processes semi-parametric estimation with predictive measure information

Adelfio, G.

Risultato della ricerca: Paper

Abstract

In this paper, we provide a method to estimate the space-time intensity of a branching-type point process by mixing nonparametric and parametric approaches. The method accounts simultaneously for the estimation of the different model components, applying a forward predictive likelihood estimation approach to semi-parametric models.
Lingua originaleEnglish
Stato di pubblicazionePublished - 2014

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Semiparametric estimation
Point process
Parametric model
Likelihood estimation

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title = "Space-time Point Processes semi-parametric estimation with predictive measure information",
abstract = "In this paper, we provide a method to estimate the space-time intensity of a branching-type point process by mixing nonparametric and parametric approaches. The method accounts simultaneously for the estimation of the different model components, applying a forward predictive likelihood estimation approach to semi-parametric models.",
author = "{Adelfio, G.} and Marcello Chiodi and Giada Adelfio",
year = "2014",
language = "English",

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TY - CONF

T1 - Space-time Point Processes semi-parametric estimation with predictive measure information

AU - Adelfio, G.

AU - Chiodi, Marcello

AU - Adelfio, Giada

PY - 2014

Y1 - 2014

N2 - In this paper, we provide a method to estimate the space-time intensity of a branching-type point process by mixing nonparametric and parametric approaches. The method accounts simultaneously for the estimation of the different model components, applying a forward predictive likelihood estimation approach to semi-parametric models.

AB - In this paper, we provide a method to estimate the space-time intensity of a branching-type point process by mixing nonparametric and parametric approaches. The method accounts simultaneously for the estimation of the different model components, applying a forward predictive likelihood estimation approach to semi-parametric models.

UR - http://hdl.handle.net/10447/133712

UR - https://aisberg.unibg.it/handle/10446/31630#.W6kVvmgzaUk

M3 - Paper

ER -