The Predictive Power of Transition Matrices

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Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_076F9F004087
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
The Predictive Power of Transition Matrices
Journal
Symmetry
Author(s)
Berchtold André
ISSN
2073-8994
Publication state
Published
Issued date
05/11/2021
Peer-reviewed
Oui
Volume
13
Number
11
Pages
2096
Language
english
Abstract
When working with Markov chains, especially if they are of order greater than one, it is often necessary to evaluate the respective contribution of each lag of the variable under study on the present. This is particularly true when using the Mixture Transition Distribution model to approximate the true fully parameterized Markov chain. Even if it is possible to evaluate each transition matrix using a standard association measure, these measures do not allow taking into account all the available information. Therefore, in this paper, we introduce a new class of so-called "predictive power" measures for transition matrices. These measures address the shortcomings of traditional association measures, so as to allow better estimation of high-order models.
Keywords
Physics and Astronomy (miscellaneous), General Mathematics, Chemistry (miscellaneous), Computer Science (miscellaneous)
Web of science
Open Access
Yes
Funding(s)
Swiss National Science Foundation / 51NF40-160590
Create date
03/01/2022 14:12
Last modification date
19/01/2022 8:08
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