A new diagnostic method for space-time point process is here introduced and applied to seismic data. It is based on the interpretation of some second-order statistics, that alow to analyze clustering features of data. In particular, this method is used to assess the goodness of fit of ETAS model, estimated by a nonparametric approach; the goal of this paper is to interpret space-time variations of seismic activity of a fixed area of Japan and to focus on clustering features that are useful for prediction purposes
ADELFIO G, CHIODI M, LUZIO D (2007). An algorithm for earthquakes clustering based on maximum likelihood. In Book of short papers – Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (pp. 301-304). Eum.
An algorithm for earthquakes clustering based on maximum likelihood
ADELFIO, Giada
;CHIODI, Marcello;LUZIO, Dario
2007-01-01
Abstract
A new diagnostic method for space-time point process is here introduced and applied to seismic data. It is based on the interpretation of some second-order statistics, that alow to analyze clustering features of data. In particular, this method is used to assess the goodness of fit of ETAS model, estimated by a nonparametric approach; the goal of this paper is to interpret space-time variations of seismic activity of a fixed area of Japan and to focus on clustering features that are useful for prediction purposes
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