Some properties of local weighted second-order statistics for spatio-temporal point processes
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Otros documentos de la autoría: adelfio, giada; Siino, Marianna; Mateu, Jorge; Rodríguez-Cortés, Francisco Javier
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Título
Some properties of local weighted second-order statistics for spatio-temporal point processesFecha de publicación
2020Editor
SpringerISSN
1436-3240; 1436-3259Cita bibliográfica
ADELFIO, Giada, et al. Some properties of local weighted second-order statistics for spatio-temporal point processes. Stochastic Environmental Research and Risk Assessment, 2020, vol. 34, núm. 1, p. 149-168Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007/s00477-019-01748-1Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Diagnostics of goodness-of-fit in the theory of point processes are often considered through the transformation of data into
residuals as a result of a thinning or a rescaling procedure. We alternatively consider ... [+]
Diagnostics of goodness-of-fit in the theory of point processes are often considered through the transformation of data into
residuals as a result of a thinning or a rescaling procedure. We alternatively consider here second-order statistics coming
from weighted measures. Motivated by Adelfio and Schoenberg (Ann Inst Stat Math 61(4):929–948, 2009) for the temporal
and spatial cases, we consider an extension to the spatio-temporal context in addition to focussing on local characteristics.
In particular, our proposed method assesses goodness-of-fit of spatio-temporal models by using local weighted secondorder statistics, computed after weighting the contribution of each observed point by the inverse of the conditional intensity
function that identifies the process. Weighted second-order statistics directly apply to data without assuming homogeneity
nor transforming the data into residuals, eliminating thus the sampling variability due to the use of a transforming
procedure. We provide some characterisations and show a number of simulation studies. [-]
Publicado en
Stochastic Environmental Research and Risk Assessment, 2020, vol. 34, núm. 1, p. 149-168Proyecto de investigación
This paper has been partially supported by the national grant of the Italian Ministry of Education University and Research (MIUR) for the PRIN-2015 program, ‘Complex space-time modelling and functional analysis for probabilistic forecast of seismic events’.Derechos de acceso
© Springer-Verlag GmbH Germany, part of Springer Nature 2019
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