A diagnostic method for space-time point process is introduced and used to interpret and assess the goodness of fit of particular models to real data such as the seismic ones. The proposed method is founded on the definition of a weighted process and allows to detect second-order features of data, like long-range dependence and fractal behavior, that are not accounted for by the fitted model. Applications to earthquake data are provided
Spatial-temporal point processes have been useful for applications in many fields, including the stu...
In this paper we aim at studying some extensions of complex space-time models, useful for the descri...
This paper develops adaptive non-parametric modelings for earthquake data. Non-parametric techniques...
A diagnostic method for space-time point process is introduced and used to interpret and assess the ...
Point processes are well studied objects in probability theory and a powerful tool in statistics fo...
A diagnostic method for space–time point process is here introduced and applied to seismic data of a...
In this paper we propose a nonparametric method, based on locally variable bandwidths kernel estima...
This paper discusses several methods to test the goodness-of-t of a space-time continuous-type branc...
In this paper we aim at studying some extensions of complex space-time models, useful for the descri...
A new diagnostic method for point processes is here presented. It is based on their second-order ana...
Spatial-temporal point process models are typically assessed using numerical summaries based on like...
The paper gives first-order residual analysis for spatiotemporal point processes that is similar to ...
Methods of examining the fit of multi-dimensional point process models using residual analysis are p...
Rescaling, thinning and superposition are useful methods for the residual analysis of spatial-tempor...
Diagnostics of goodness-of-fit in the theory of point processes are often considered through the tra...
Spatial-temporal point processes have been useful for applications in many fields, including the stu...
In this paper we aim at studying some extensions of complex space-time models, useful for the descri...
This paper develops adaptive non-parametric modelings for earthquake data. Non-parametric techniques...
A diagnostic method for space-time point process is introduced and used to interpret and assess the ...
Point processes are well studied objects in probability theory and a powerful tool in statistics fo...
A diagnostic method for space–time point process is here introduced and applied to seismic data of a...
In this paper we propose a nonparametric method, based on locally variable bandwidths kernel estima...
This paper discusses several methods to test the goodness-of-t of a space-time continuous-type branc...
In this paper we aim at studying some extensions of complex space-time models, useful for the descri...
A new diagnostic method for point processes is here presented. It is based on their second-order ana...
Spatial-temporal point process models are typically assessed using numerical summaries based on like...
The paper gives first-order residual analysis for spatiotemporal point processes that is similar to ...
Methods of examining the fit of multi-dimensional point process models using residual analysis are p...
Rescaling, thinning and superposition are useful methods for the residual analysis of spatial-tempor...
Diagnostics of goodness-of-fit in the theory of point processes are often considered through the tra...
Spatial-temporal point processes have been useful for applications in many fields, including the stu...
In this paper we aim at studying some extensions of complex space-time models, useful for the descri...
This paper develops adaptive non-parametric modelings for earthquake data. Non-parametric techniques...