The Predictive Power of Transition Matrices

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 t...

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Autor principal:	André Berchtold
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	predictive power measure of association transition matrix Markov chain MTD model Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/61a092af93754d3fa9c45828bf8f5a71
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id	oai:doaj.org-article:61a092af93754d3fa9c45828bf8f5a71
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spelling	oai:doaj.org-article:61a092af93754d3fa9c45828bf8f5a712021-11-25T19:06:43ZThe Predictive Power of Transition Matrices10.3390/sym131120962073-8994https://doaj.org/article/61a092af93754d3fa9c45828bf8f5a712021-11-01T00:00:00Zhttps://www.mdpi.com/2073-8994/13/11/2096https://doaj.org/toc/2073-8994When 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.André BerchtoldMDPI AGarticlepredictive powermeasure of associationtransition matrixMarkov chainMTD modelMathematicsQA1-939ENSymmetry, Vol 13, Iss 2096, p 2096 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	predictive power measure of association transition matrix Markov chain MTD model Mathematics QA1-939
spellingShingle	predictive power measure of association transition matrix Markov chain MTD model Mathematics QA1-939 André Berchtold The Predictive Power of Transition Matrices
description	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.
format	article
author	André Berchtold
author_facet	André Berchtold
author_sort	André Berchtold
title	The Predictive Power of Transition Matrices
title_short	The Predictive Power of Transition Matrices
title_full	The Predictive Power of Transition Matrices
title_fullStr	The Predictive Power of Transition Matrices
title_full_unstemmed	The Predictive Power of Transition Matrices
title_sort	predictive power of transition matrices
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/61a092af93754d3fa9c45828bf8f5a71
work_keys_str_mv	AT andreberchtold thepredictivepoweroftransitionmatrices AT andreberchtold predictivepoweroftransitionmatrices
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The Predictive Power of Transition Matrices

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