Displaying similar documents to “Spatio-temporal modelling of a Cox point process sampled by a curve, filtering and Inference”

Hazard rate model and statistical analysis of a compound point process

Petr Volf (2005)

Kybernetika

Similarity:

A stochastic process cumulating random increments at random moments is studied. We model it as a two-dimensional random point process and study advantages of such an approach. First, a rather general model allowing for the dependence of both components mutually as well as on covariates is formulated, then the case where the increments depend on time is analyzed with the aid of the multiplicative hazard regression model. Special attention is devoted to the problem of prediction of process...

Alpha-stable branching and beta-coalescents.

Birkner, Matthias, Blath, Jochen, Capaldo, Marcella, Etheridge, Alison M., Möhle, Martin, Schweinsberg, Jason, Wakolbinger, Anton (2005)

Electronic Journal of Probability [electronic only]

Similarity:

Superposition of diffusions with linear generator and its multifractal limit process

End Iglói, György Terdik (2003)

ESAIM: Probability and Statistics

Similarity:

In this paper a new multifractal stochastic process called Limit of the Integrated Superposition of Diffusion processes with Linear differencial Generator (LISDLG) is presented which realistically characterizes the network traffic multifractality. Several properties of the LISDLG model are presented including long range dependence, cumulants, logarithm of the characteristic function, dilative stability, spectrum and bispectrum. The model captures higher-order statistics by the cumulants....

On cumulative process model and its statistical analysis

Petr Volf (2000)

Kybernetika

Similarity:

The notion of the counting process is recalled and the idea of the ‘cumulative’ process is presented. While the counting process describes the sequence of events, by the cumulative process we understand a stochastic process which cumulates random increments at random moments. It is described by an intensity of the random (counting) process of these moments and by a distribution of increments. We derive the martingale – compensator decomposition of the process and then we study the estimator...