This paper treats functional marked point processes (FMPPs), which are defined asmarked point processes where the marks are random elements in some (Polish) func-tion space. Such marks may represent, for example, spatial paths or functions of time.To be able to consider, for example, multivariate FMPPs, we also attach an additional,Euclidean, mark to each point. We indicate how the FMPP framework quite naturallyconnects the point process framework with both the functional data analysis frame-work and the geostatistical framework. We further show that various existing stochasticmodels fit well into the FMPP framework. To be able to carry out nonparametric sta-tistical analyses for FMPPs, we study characteristics such as product densities and...
A spatial marked point process describes the locations of randomly distributed events in a region, w...
Observing complete functions as a result of random experiments is nowadays possible by the developme...
Large-scale event time data recorded with high temporal resolutions have become increasingly availab...
This paper treats functional marked point processes (FMPPs), which are defined as marked point proce...
This paper defines the class of càdlàg functional marked point processes (CFMPPs). These are (spati...
This paper defines the class of càdlàg functional marked point processes (CFMPPs). These are (spatio...
The underlying idea of this thesis is to move from a spatio-temporal process to a functional spatio-...
We present a family of local inhomogeneous mark-weighted summary statistics for general marked point...
This paper is focus on spatial functional variables whose observations are a set of spatially correl...
We propose new summary statistics for intensity-reweighted moment stationary point processes, that ...
We propose a new summary statistic for inhomogeneous intensity-reweighted moment stationarity spatio...
We present and compare functional and spatio-temporal (Sp.T.) kriging approaches to predict spatial ...
A new second order statistic based on the mark correlation function to analyse spatial p...
We introduce a simple and interpretable model for functional data analysis for situations where the ...
We propose new summary statistics for intensity-reweighted moment stationary point processes that ge...
A spatial marked point process describes the locations of randomly distributed events in a region, w...
Observing complete functions as a result of random experiments is nowadays possible by the developme...
Large-scale event time data recorded with high temporal resolutions have become increasingly availab...
This paper treats functional marked point processes (FMPPs), which are defined as marked point proce...
This paper defines the class of càdlàg functional marked point processes (CFMPPs). These are (spati...
This paper defines the class of càdlàg functional marked point processes (CFMPPs). These are (spatio...
The underlying idea of this thesis is to move from a spatio-temporal process to a functional spatio-...
We present a family of local inhomogeneous mark-weighted summary statistics for general marked point...
This paper is focus on spatial functional variables whose observations are a set of spatially correl...
We propose new summary statistics for intensity-reweighted moment stationary point processes, that ...
We propose a new summary statistic for inhomogeneous intensity-reweighted moment stationarity spatio...
We present and compare functional and spatio-temporal (Sp.T.) kriging approaches to predict spatial ...
A new second order statistic based on the mark correlation function to analyse spatial p...
We introduce a simple and interpretable model for functional data analysis for situations where the ...
We propose new summary statistics for intensity-reweighted moment stationary point processes that ge...
A spatial marked point process describes the locations of randomly distributed events in a region, w...
Observing complete functions as a result of random experiments is nowadays possible by the developme...
Large-scale event time data recorded with high temporal resolutions have become increasingly availab...