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Title: Graphical model selection for a particular class of continuous-time processes (English)
Author: Zorzi, Mattia
Language: English
Journal: Kybernetika
ISSN: 0023-5954 (print)
ISSN: 1805-949X (online)
Volume: 55
Issue: 5
Year: 2019
Pages: 782-801
Summary lang: English
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Category: math
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Summary: 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. (English)
Keyword: sparse inverse covariance selection
Keyword: regularization
Keyword: graphical models
Keyword: entropy
Keyword: optimization
MSC: 65K10
MSC: 93B30
idZBL: Zbl 07177916
idMR: MR4055576
DOI: 10.14736/kyb-2019-5-0782
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Date available: 2020-01-06T11:21:13Z
Last updated: 2020-11-23
Stable URL: http://hdl.handle.net/10338.dmlcz/147951
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