A two-stage maximum entropy approach for time series regression

Bibliographic Details
Main Author: Macedo, Pedro
Publication Date: 2024
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10773/41566
Summary: The maximum entropy bootstrap for time series is a technique that creates a large number of replicates, as elements of an ensemble, for inference purposes, which satisfies the ergodic and the central limit theorems. As an alternative to the use of traditional techniques, this work proposes generalized maximum entropy for the estimation of parameters in all the replicated models. An empirical application and a simulated example illustrate the advantages of this two-stage maximum entropy approach for time series regression modeling, where maximum entropy is used both in data replication and in parameter estimation.
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spelling A two-stage maximum entropy approach for time series regressionBootstrapIll-conditioned modelsInfo-metricsTime series regressionThe maximum entropy bootstrap for time series is a technique that creates a large number of replicates, as elements of an ensemble, for inference purposes, which satisfies the ergodic and the central limit theorems. As an alternative to the use of traditional techniques, this work proposes generalized maximum entropy for the estimation of parameters in all the replicated models. An empirical application and a simulated example illustrate the advantages of this two-stage maximum entropy approach for time series regression modeling, where maximum entropy is used both in data replication and in parameter estimation.Taylor and Francis2024-04-17T09:36:25Z2024-01-01T00:00:00Z2024info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/41566eng0361-091810.1080/03610918.2022.2057540Macedo, Pedroinfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-06T04:55:58Zoai:ria.ua.pt:10773/41566Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T14:24:19.973086Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv A two-stage maximum entropy approach for time series regression
title A two-stage maximum entropy approach for time series regression
spellingShingle A two-stage maximum entropy approach for time series regression
Macedo, Pedro
Bootstrap
Ill-conditioned models
Info-metrics
Time series regression
title_short A two-stage maximum entropy approach for time series regression
title_full A two-stage maximum entropy approach for time series regression
title_fullStr A two-stage maximum entropy approach for time series regression
title_full_unstemmed A two-stage maximum entropy approach for time series regression
title_sort A two-stage maximum entropy approach for time series regression
author Macedo, Pedro
author_facet Macedo, Pedro
author_role author
dc.contributor.author.fl_str_mv Macedo, Pedro
dc.subject.por.fl_str_mv Bootstrap
Ill-conditioned models
Info-metrics
Time series regression
topic Bootstrap
Ill-conditioned models
Info-metrics
Time series regression
description The maximum entropy bootstrap for time series is a technique that creates a large number of replicates, as elements of an ensemble, for inference purposes, which satisfies the ergodic and the central limit theorems. As an alternative to the use of traditional techniques, this work proposes generalized maximum entropy for the estimation of parameters in all the replicated models. An empirical application and a simulated example illustrate the advantages of this two-stage maximum entropy approach for time series regression modeling, where maximum entropy is used both in data replication and in parameter estimation.
publishDate 2024
dc.date.none.fl_str_mv 2024-04-17T09:36:25Z
2024-01-01T00:00:00Z
2024
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10773/41566
url http://hdl.handle.net/10773/41566
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0361-0918
10.1080/03610918.2022.2057540
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Taylor and Francis
publisher.none.fl_str_mv Taylor and Francis
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833594567880867840

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