This paper proposes the use of Integrated Nested Laplace Approximation (Rue et al., 2009) to describe the spatial displacement of earthquake data. Specifying a hiechical structure of the data and parameters, an inhomogeneuos Log-Gaussian Cox Processes model is applied for describing seismic events occurred in Greece, an area of seismic hazard. In this way, the dependence of the spatial point process on external covariates can be taken into account, as well as the interaction among points, through the estimation of the parameters of the covariance of the Gaussian Random Field, with a computationally efficient approach
Methods of examining the fit of multi-dimensional point process models using residual analysis are p...
The goal of this paper is to derive a hazard map for earthquake occurrences in Pakistan from a catal...
Many objects in space can best be modeled statistically by using point processes. Examples are fires...
In this paper, we propose the use of advanced and flexible statistical models to describe the spatia...
Using recent results for local composite likelihood for spatial point processes, we show the perform...
In this paper we aim at studying some extensions of complex space-time models, useful for the descri...
This paper develops methodology that provides a toolbox for routinely fitting complex models to real...
In this paper we aim at studying some extensions of complex space-time models, useful for the descri...
We propose a local version of the spatio-temporal log-Gaussian Cox processes (LGCPs) employing the L...
Cox processes are natural models for point process phenomena that are environmentally driven, but mu...
Earthquakes can be seen as realization of a spatial, temporal, or spatiotemporal point process. Give...
This work presents an application of spatio-temporal log-Gaussian Cox processes for the description...
Earthquakes are one of the most destructive natural disasters and the spatial distribution of their ...
Seismic networks provide data that are used as basis both for public safety decisions and for scient...
Abstract: Spatial statistics is concerned with statistical methods that explicitly analyses spatial ...
Methods of examining the fit of multi-dimensional point process models using residual analysis are p...
The goal of this paper is to derive a hazard map for earthquake occurrences in Pakistan from a catal...
Many objects in space can best be modeled statistically by using point processes. Examples are fires...
In this paper, we propose the use of advanced and flexible statistical models to describe the spatia...
Using recent results for local composite likelihood for spatial point processes, we show the perform...
In this paper we aim at studying some extensions of complex space-time models, useful for the descri...
This paper develops methodology that provides a toolbox for routinely fitting complex models to real...
In this paper we aim at studying some extensions of complex space-time models, useful for the descri...
We propose a local version of the spatio-temporal log-Gaussian Cox processes (LGCPs) employing the L...
Cox processes are natural models for point process phenomena that are environmentally driven, but mu...
Earthquakes can be seen as realization of a spatial, temporal, or spatiotemporal point process. Give...
This work presents an application of spatio-temporal log-Gaussian Cox processes for the description...
Earthquakes are one of the most destructive natural disasters and the spatial distribution of their ...
Seismic networks provide data that are used as basis both for public safety decisions and for scient...
Abstract: Spatial statistics is concerned with statistical methods that explicitly analyses spatial ...
Methods of examining the fit of multi-dimensional point process models using residual analysis are p...
The goal of this paper is to derive a hazard map for earthquake occurrences in Pakistan from a catal...
Many objects in space can best be modeled statistically by using point processes. Examples are fires...