Joint second-order parameter estimation for spatio-temporal log-Gaussian Cox processes
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https://doi.org/10.1007/s00477-018-1579-0 |
Metadatos
Título
Joint second-order parameter estimation for spatio-temporal log-Gaussian Cox processesFecha de publicación
2018Editor
SpringerISSN
1436-3240; 1436-3259Cita bibliográfica
Siino, Marianna; Adelfio, Giada; Mateu, Jorge. "Joint second-order parameter estimation for spatio-temporal log-Gaussian Cox processes." Stochastic Environmental Research and Risk Assessment, 2018, vol. 32, núm. 12, p. 3525-3539Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007/s00477-018-1579-0Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
We propose a new fitting method to estimate the set of second-order parameters for the class of homogeneous spatio-temporal log-Gaussian Cox point processes. With simulations, we show that the proposed minimum contrast ... [+]
We propose a new fitting method to estimate the set of second-order parameters for the class of homogeneous spatio-temporal log-Gaussian Cox point processes. With simulations, we show that the proposed minimum contrast procedure,based on the spatio-temporal pair correlation function, provides reliable estimates and we compare the results with thecurrent available methods. Moreover, the proposed method can be used in the case of both separable and non-separableparametric specifications of the correlation function of the underlying Gaussian Random Field. We describe earthquakesequences comparing several Cox model specifications. [-]
Publicado en
Stochastic Environmental Research and Risk Assessment, 2018, vol. 32, núm. 12, p. 3525-3539Proyecto de investigación
This paper has been supported by the national grant of the Italian Ministry of Education University and Research (MIUR) for the PRIN-2015 program (Progetti di ricerca di Rilevante Interesse Nazionale), “Prot. 20157PRZC4— Research Project Title Complex space-time modeling and functional analysis for probabilistic forecast of seismic events. PI: Giada Adelfio”. J. Mateu has been partially founded by Grants P1-1B2015-40 and MTM2016-78917-RDerechos de acceso
© Springer-Verlag GmbH Germany, part of Springer Nature 2018
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