An estimation approach for the semi-param-etric intensity function of a class of space-time point processes is introduced. In particular we want to account for the estimation of parametric and nonparametric components simultaneously, applying a forward predictive likelihood to semi-parametric models. For each event, the probability of being a background event or an offspring is therefore estimated
We are dealing with regression models for point processes having a multiplicative intensity process ...
The conditional intensity function of a space-time branching model is defined by the sum of two main...
The spatio-temporal epidemic type aftershock sequence (ETAS) model is widely used to describe the se...
An estimation approach for the semi-param-etric intensity function of a class of space-time 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-parametric intensity function of a particular space-time point p...
In this paper we propose a clustering technique, based on the maximization of the likelihood functio...
Point processes are well studied objects in probability theory and a powerful tool in statistics for...
etasFLP is an R package which fits an epidemic type aftershock sequence (ETAS) model to an earthquak...
The paper proposes a stochastic process that improves the assessment of events in space and time, c...
etasFLP is an R package which fits an epidemic type aftershock sequence (ETAS) model to an earthquak...
Dealing with data from a space–time point process, the estimation of the conditional intensity funct...
In this paper we propose a nonparametric method, based on locally variable bandwidths kernel estima...
Point processes models describe random sequences of events. One key model is the self-exciting point...
Introduction Our basic object is a series of recurrent events, occurring at random times ø 1 ; ø 2 ...
We are dealing with regression models for point processes having a multiplicative intensity process ...
The conditional intensity function of a space-time branching model is defined by the sum of two main...
The spatio-temporal epidemic type aftershock sequence (ETAS) model is widely used to describe the se...
An estimation approach for the semi-param-etric intensity function of a class of space-time 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-parametric intensity function of a particular space-time point p...
In this paper we propose a clustering technique, based on the maximization of the likelihood functio...
Point processes are well studied objects in probability theory and a powerful tool in statistics for...
etasFLP is an R package which fits an epidemic type aftershock sequence (ETAS) model to an earthquak...
The paper proposes a stochastic process that improves the assessment of events in space and time, c...
etasFLP is an R package which fits an epidemic type aftershock sequence (ETAS) model to an earthquak...
Dealing with data from a space–time point process, the estimation of the conditional intensity funct...
In this paper we propose a nonparametric method, based on locally variable bandwidths kernel estima...
Point processes models describe random sequences of events. One key model is the self-exciting point...
Introduction Our basic object is a series of recurrent events, occurring at random times ø 1 ; ø 2 ...
We are dealing with regression models for point processes having a multiplicative intensity process ...
The conditional intensity function of a space-time branching model is defined by the sum of two main...
The spatio-temporal epidemic type aftershock sequence (ETAS) model is widely used to describe the se...