Graphical model selection for a particular class of continuous-time processes

Mattia Zorzi

Kybernetika (2019)

  • Volume: 55, Issue: 5, page 782-801
  • ISSN: 0023-5954

Abstract

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Graphical models provide an undirected graph representation of relations between the components of a random vector. In the Gaussian case such an undirected graph is used to describe conditional independence relations among such components. In this paper, we consider a continuous-time Gaussian model which is accessible to observations only at time T . We introduce the concept of infinitesimal conditional independence for such a model. Then, we address the corresponding graphical model selection problem, i. e. the problem to estimate the graphical model from data. Finally, simulation studies are proposed to test the effectiveness of the graphical model selection procedure.

How to cite

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Zorzi, Mattia. "Graphical model selection for a particular class of continuous-time processes." Kybernetika 55.5 (2019): 782-801. <http://eudml.org/doc/295064>.

@article{Zorzi2019,
abstract = {Graphical models provide an undirected graph representation of relations between the components of a random vector. In the Gaussian case such an undirected graph is used to describe conditional independence relations among such components. In this paper, we consider a continuous-time Gaussian model which is accessible to observations only at time $T$. We introduce the concept of infinitesimal conditional independence for such a model. Then, we address the corresponding graphical model selection problem, i. e. the problem to estimate the graphical model from data. Finally, simulation studies are proposed to test the effectiveness of the graphical model selection procedure.},
author = {Zorzi, Mattia},
journal = {Kybernetika},
keywords = {sparse inverse covariance selection; regularization; graphical models; entropy; optimization},
language = {eng},
number = {5},
pages = {782-801},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Graphical model selection for a particular class of continuous-time processes},
url = {http://eudml.org/doc/295064},
volume = {55},
year = {2019},
}

TY - JOUR
AU - Zorzi, Mattia
TI - Graphical model selection for a particular class of continuous-time processes
JO - Kybernetika
PY - 2019
PB - Institute of Information Theory and Automation AS CR
VL - 55
IS - 5
SP - 782
EP - 801
AB - Graphical models provide an undirected graph representation of relations between the components of a random vector. In the Gaussian case such an undirected graph is used to describe conditional independence relations among such components. In this paper, we consider a continuous-time Gaussian model which is accessible to observations only at time $T$. We introduce the concept of infinitesimal conditional independence for such a model. Then, we address the corresponding graphical model selection problem, i. e. the problem to estimate the graphical model from data. Finally, simulation studies are proposed to test the effectiveness of the graphical model selection procedure.
LA - eng
KW - sparse inverse covariance selection; regularization; graphical models; entropy; optimization
UR - http://eudml.org/doc/295064
ER -

References

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