Add to ORKGIn this paper, we provide a method to estimate the space-time intensity of a branching-type point process by mixing nonparametric and parametric approaches. The method accounts simultaneously for the estimation of the different model components, applying a forward predictive likelihood estimation approach to semi-parametric models
When dealing with data coming from a space time inhomogeneous process, there is often the need of o...
Dealing with data coming from a space-time inhomogeneous process, there is often the need of obtaini...
Fitting of parametric models to spatial and space-time point patterns has been a very active researc...
In this paper, we provide a method to estimate the space-time intensity of a branching-type point pr...
In this paper, we provide a method to estimate the space-time intensity of a branching-type point pr...
An estimation approach for the semi-param-etric intensity function of a class of space-time point p...
Dealing with data from a space\u2013time point process, the estimation of the conditional intensity ...
The conditional intensity function of a space-time branching model is defined by the sum of two main...
An estimation approach for the semi-parametric intensity function of a particular space-time point p...
Point processes are well studied objects in probability theory and a powerful tool in statistics for...
Model-based inferential methods for point processes have received less attention than the correspond...
Introduction Our basic object is a series of recurrent events, occurring at random times ø 1 ; ø 2 ...
The thesis deals with point processes of objects with random lifetime. The form of the likelihood fu...
We are dealing with series of events occurring at random times #tau#_n and carrying further quantiti...
This paper is concerned with combined inference for point processes on the real line observed in a b...
When dealing with data coming from a space time inhomogeneous process, there is often the need of o...
Dealing with data coming from a space-time inhomogeneous process, there is often the need of obtaini...
Fitting of parametric models to spatial and space-time point patterns has been a very active researc...
In this paper, we provide a method to estimate the space-time intensity of a branching-type point pr...
In this paper, we provide a method to estimate the space-time intensity of a branching-type point pr...
An estimation approach for the semi-param-etric intensity function of a class of space-time point p...
Dealing with data from a space\u2013time point process, the estimation of the conditional intensity ...
The conditional intensity function of a space-time branching model is defined by the sum of two main...
An estimation approach for the semi-parametric intensity function of a particular space-time point p...
Point processes are well studied objects in probability theory and a powerful tool in statistics for...
Model-based inferential methods for point processes have received less attention than the correspond...
Introduction Our basic object is a series of recurrent events, occurring at random times ø 1 ; ø 2 ...
The thesis deals with point processes of objects with random lifetime. The form of the likelihood fu...
We are dealing with series of events occurring at random times #tau#_n and carrying further quantiti...
This paper is concerned with combined inference for point processes on the real line observed in a b...
When dealing with data coming from a space time inhomogeneous process, there is often the need of o...
Dealing with data coming from a space-time inhomogeneous process, there is often the need of obtaini...
Fitting of parametric models to spatial and space-time point patterns has been a very active researc...