0000000000425965
AUTHOR
Adelfio G
On Some Statistical Properties of the Spatio-Temporal Product Density
We present an extension of the non-parametric edge-corrected Ohser-type kernel estimator for the spatio-temporal product density function. We derive the mean and variance of the estimator and give a closed-form approximation for a spatio-temporal Poisson point process. Asymptotic properties of this second-order characteristic are derived, using an approach based on martingale theory. Taking advantage of the convergence to normality, confidence surfaces under the homogeneous Poisson process are built. A simulation study is presented to compare our approximation for the variance with Monte Carlo estimated values. Finally, we apply the resulting estimator and its properties to analyse the spat…
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.
Approximate Bayesian Computation for Forecasting in Hydrological models
Approximate Bayesian Computation (ABC) is a statistical tool for handling parameter inference in a range of challenging statistical problems, mostly characterized by an intractable likelihood function. In this paper, we focus on the application of ABC to hydrological models, not as a tool for parametric inference, but as a mechanism for generating probabilistic forecasts. This mechanism is referred as Approximate Bayesian Forecasting (ABF). The abcd water balance model is applied to a case study on Aipe river basin in Columbia to demonstrate the applicability of ABF. The predictivity of the ABF is compared with the predictivity of the MCMC algorithm. The results show that the ABF method as …
Spatial analysis of the Italian seismic network and seismicity
Seismic networks are powerful tools for understanding active tectonic processes in a monitored region. Their numerous applications, from monitoring seismicity to characterizing seismogenic volumes and generated seismicity, make seismic networks essential tools for assessing seismic hazard in active regions. The ability to locate earthquakes hypocenters requires a seismic network with a sufficient number of optimally distributed, stations. It is important to assess existing network geometry, to identify seismogenic volumes that are not adequately monitored, and to quantify measures that will allow network improvement. In this work we have studied the spatial arrangement of the stations of th…
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 …