0000000000502065
AUTHOR
Chiodi M
A space-time branching process with covariates
The paper proposes a stochastic process that improves the assessment of seismic events in space and time, considering a contagion model (branching process) within a regression-like framework. The proposed approach develops the Forward Likelihood for prediction (FLP) method including covariates in the epidemic component.
A PARTITION TYPE METHOD FOR CLUSTERING MIXED DATA
In this paper, we propose a method for clustering mixed data. The method is a nonhierarchical one, and deals simultaneously with variables of three main kinds: numerical, ordinal, and nominal. It is based on the minimization of a particular criterion f(G。) over all the partitions G。of n entities in m distinct clusters. The criterion is founded on a peculiar kind of internal standardized mean diversity of the entities, according to the three types of variables. The algorithm to get the best partition is also presented: it starts from a non-random choice of the first partition; the results are compared with those obtained by a random assignment to a first partition. In order to show the usefu…
Multiscale processes to describe the Eastern Sicily Seismic Sequences
In this paper, a version of hybrid of Gibbs point process models is proposed as method to characterise the multiscale interaction structure of several seismic sequences occurred in the Eastern Sicily in the last decade. Seismic sequences were identified by a clustering technique based on space-time distance criterion and hierarchical clustering. We focus our analysis on five small seismic sequences, showing that two of these are described by an inhomogeneous Poisson process (not significant interaction among events) while the other three clusters are described by a hybrid-Geyer process (mutiscale interaction between events). The proposed method, although it still needs extensive testing on …