Multivariate Adaptive Regression Splines (MARS) for non-linear time series dependence: An application in biostatistics

Over several years, time series analysis using ARIMA modelling has been proposed as a suitable method to investigate the nonlinear relationship between antimicrobial use and resistance. Nonetheless, these studies did not mention the thresholds of antibiotic use to manage the precise prescription. Th...

Descripción completa

Detalles Bibliográficos
Autor: Pham Thi Van, Ha
Tipo de recurso: tesis de maestría
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/348071
Acceso en línea:https://hdl.handle.net/2117/348071
Access Level:acceso abierto
Palabra clave:Statistical mechanics
MARS
TF
Antimicrobial use and resistance rate
Mecànica estadística
Classificació AMS::82 Statistical mechanics, structure of matter::82C Time-dependent statistical mechanics (dynamic and nonequilibrium)
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
id ES_22e92f4e4f033664f34ed6a6d71b47e7
oai_identifier_str oai:upcommons.upc.edu:2117/348071
network_acronym_str ES
network_name_str España
repository_id_str
spelling Multivariate Adaptive Regression Splines (MARS) for non-linear time series dependence: An application in biostatisticsPham Thi Van, HaStatistical mechanicsMARSTFAntimicrobial use and resistance rateMecànica estadísticaClassificació AMS::82 Statistical mechanics, structure of matter::82C Time-dependent statistical mechanics (dynamic and nonequilibrium)Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàticaOver several years, time series analysis using ARIMA modelling has been proposed as a suitable method to investigate the nonlinear relationship between antimicrobial use and resistance. Nonetheless, these studies did not mention the thresholds of antibiotic use to manage the precise prescription. This paper explores using multivariate adaptive regression splines for nonlinear time series to develop models applying monthly antimicrobial resistance and antimicrobial use data. And according to the study of the MARS methodology with the application to antibiotic use and resistance, this type of model is proved to be useful to identify relationships between explanatory and outcome time series. The approach is very flexible and allows for the inclusion of smooth functions, thresholds and lags in the input time series that reflect non-linear dependencies.Universitat Politècnica de CatalunyaSánchez Espigares, Josep Anton20212021-06-0120212021-06-29master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2117/348071reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3480712026-05-27T15:37:01Z
dc.title.none.fl_str_mv Multivariate Adaptive Regression Splines (MARS) for non-linear time series dependence: An application in biostatistics
title Multivariate Adaptive Regression Splines (MARS) for non-linear time series dependence: An application in biostatistics
spellingShingle Multivariate Adaptive Regression Splines (MARS) for non-linear time series dependence: An application in biostatistics
Pham Thi Van, Ha
Statistical mechanics
MARS
TF
Antimicrobial use and resistance rate
Mecànica estadística
Classificació AMS::82 Statistical mechanics, structure of matter::82C Time-dependent statistical mechanics (dynamic and nonequilibrium)
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
title_short Multivariate Adaptive Regression Splines (MARS) for non-linear time series dependence: An application in biostatistics
title_full Multivariate Adaptive Regression Splines (MARS) for non-linear time series dependence: An application in biostatistics
title_fullStr Multivariate Adaptive Regression Splines (MARS) for non-linear time series dependence: An application in biostatistics
title_full_unstemmed Multivariate Adaptive Regression Splines (MARS) for non-linear time series dependence: An application in biostatistics
title_sort Multivariate Adaptive Regression Splines (MARS) for non-linear time series dependence: An application in biostatistics
dc.creator.none.fl_str_mv Pham Thi Van, Ha
author Pham Thi Van, Ha
author_facet Pham Thi Van, Ha
author_role author
dc.contributor.none.fl_str_mv Sánchez Espigares, Josep Anton
dc.subject.none.fl_str_mv Statistical mechanics
MARS
TF
Antimicrobial use and resistance rate
Mecànica estadística
Classificació AMS::82 Statistical mechanics, structure of matter::82C Time-dependent statistical mechanics (dynamic and nonequilibrium)
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
topic Statistical mechanics
MARS
TF
Antimicrobial use and resistance rate
Mecànica estadística
Classificació AMS::82 Statistical mechanics, structure of matter::82C Time-dependent statistical mechanics (dynamic and nonequilibrium)
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
description Over several years, time series analysis using ARIMA modelling has been proposed as a suitable method to investigate the nonlinear relationship between antimicrobial use and resistance. Nonetheless, these studies did not mention the thresholds of antibiotic use to manage the precise prescription. This paper explores using multivariate adaptive regression splines for nonlinear time series to develop models applying monthly antimicrobial resistance and antimicrobial use data. And according to the study of the MARS methodology with the application to antibiotic use and resistance, this type of model is proved to be useful to identify relationships between explanatory and outcome time series. The approach is very flexible and allows for the inclusion of smooth functions, thresholds and lags in the input time series that reflect non-linear dependencies.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-06-01
2021
2021-06-29
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/348071
url https://hdl.handle.net/2117/348071
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869404608969834496
score 15.300719

Similares en LA Referencia