Modelado de cuantiles marginales en presencia de datos faltantes mediante la clase de modelos de regresión con distribución normal/independiente multivariada

En este trabajo de investigación, se propone el desarrollo de un modelo de regresión lineal con respuesta multivariada asociado a la clase de distribuciones normal/independiente multivariadas. El objetivo principal es lograr el modelado de cuantiles marginales bajo la presencia de datos faltantes, t...

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Autores:
Escobar Arias, Jose Antonio
Tipo de recurso:
Fecha de publicación:
2024
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/86279
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/86279
https://repositorio.unal.edu.co/
Palabra clave:
510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
Modelos log-lineales
Análisis de regresión
Análisis multivariante
Algoritmo de Aumento de Datos Monótonos (MDA Algorithm)
distribuciónn normal/independiente multivariada
distribución log-normal/independiente multivariada
modelado de cuantiles
datos faltantes
regresión lineal multivariada
Monotone Data Augmentation Algorithm (MDA Algorithm)
multivariate normal/independent distribution
multivariate log-normal/independent distribution
quantile modeling
missing data
multivariate linear regression
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openAccess
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Atribución-NoComercial 4.0 Internacional
id UNACIONAL2_6739be4bcf26b788faac139dfdc296c1
oai_identifier_str oai:repositorio.unal.edu.co:unal/86279
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Modelado de cuantiles marginales en presencia de datos faltantes mediante la clase de modelos de regresión con distribución normal/independiente multivariada
dc.title.translated.eng.fl_str_mv Modeling of marginal quantiles in the presence of missing data using the class of regression models with normal/independent multivariate distribution
title Modelado de cuantiles marginales en presencia de datos faltantes mediante la clase de modelos de regresión con distribución normal/independiente multivariada
spellingShingle Modelado de cuantiles marginales en presencia de datos faltantes mediante la clase de modelos de regresión con distribución normal/independiente multivariada
510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
Modelos log-lineales
Análisis de regresión
Análisis multivariante
Algoritmo de Aumento de Datos Monótonos (MDA Algorithm)
distribuciónn normal/independiente multivariada
distribución log-normal/independiente multivariada
modelado de cuantiles
datos faltantes
regresión lineal multivariada
Monotone Data Augmentation Algorithm (MDA Algorithm)
multivariate normal/independent distribution
multivariate log-normal/independent distribution
quantile modeling
missing data
multivariate linear regression
title_short Modelado de cuantiles marginales en presencia de datos faltantes mediante la clase de modelos de regresión con distribución normal/independiente multivariada
title_full Modelado de cuantiles marginales en presencia de datos faltantes mediante la clase de modelos de regresión con distribución normal/independiente multivariada
title_fullStr Modelado de cuantiles marginales en presencia de datos faltantes mediante la clase de modelos de regresión con distribución normal/independiente multivariada
title_full_unstemmed Modelado de cuantiles marginales en presencia de datos faltantes mediante la clase de modelos de regresión con distribución normal/independiente multivariada
title_sort Modelado de cuantiles marginales en presencia de datos faltantes mediante la clase de modelos de regresión con distribución normal/independiente multivariada
dc.creator.fl_str_mv Escobar Arias, Jose Antonio
dc.contributor.advisor.none.fl_str_mv Mazo Lopera, Mauricio Alejandro
dc.contributor.author.none.fl_str_mv Escobar Arias, Jose Antonio
dc.subject.ddc.spa.fl_str_mv 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
topic 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
Modelos log-lineales
Análisis de regresión
Análisis multivariante
Algoritmo de Aumento de Datos Monótonos (MDA Algorithm)
distribuciónn normal/independiente multivariada
distribución log-normal/independiente multivariada
modelado de cuantiles
datos faltantes
regresión lineal multivariada
Monotone Data Augmentation Algorithm (MDA Algorithm)
multivariate normal/independent distribution
multivariate log-normal/independent distribution
quantile modeling
missing data
multivariate linear regression
dc.subject.lemb.none.fl_str_mv Modelos log-lineales
Análisis de regresión
Análisis multivariante
dc.subject.proposal.spa.fl_str_mv Algoritmo de Aumento de Datos Monótonos (MDA Algorithm)
distribuciónn normal/independiente multivariada
distribución log-normal/independiente multivariada
modelado de cuantiles
datos faltantes
regresión lineal multivariada
dc.subject.proposal.eng.fl_str_mv Monotone Data Augmentation Algorithm (MDA Algorithm)
multivariate normal/independent distribution
multivariate log-normal/independent distribution
quantile modeling
missing data
multivariate linear regression
description En este trabajo de investigación, se propone el desarrollo de un modelo de regresión lineal con respuesta multivariada asociado a la clase de distribuciones normal/independiente multivariadas. El objetivo principal es lograr el modelado de cuantiles marginales bajo la presencia de datos faltantes, teniendo en cuenta la asociación entre las variables del vector de respuesta. Se emplea un enfoque Bayesiano, aprovechando las herramientas que este ofrece, como también algoritmos (que serán descritos posteriormente) para llevar a cabo el proceso de imputación y aproximación de distribuciones posteriores. La validez del modelo se evalúa mediante estudios de simulación, que confirman el desempeño satisfactorio en el proceso de estimación de los parámetros. Además, se presenta una aplicación práctica del modelo a un conjunto de datos reales, proporcionando así una validación adicional de su utilidad y aplicabilidad en contextos empíricos. (Tomado de la fuente)
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-06-19T21:06:15Z
dc.date.available.none.fl_str_mv 2024-06-19T21:06:15Z
dc.date.issued.none.fl_str_mv 2024
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/86279
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/86279
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.indexed.spa.fl_str_mv LaReferencia
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dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
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dc.format.extent.spa.fl_str_mv 94 páginas
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dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv Medellín - Ciencias - Maestría en Ciencias - Estadística
dc.publisher.faculty.spa.fl_str_mv Facultad de Ciencias
dc.publisher.place.spa.fl_str_mv Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv Universidad Nacional de Colombia - Sede Medellín
institution Universidad Nacional de Colombia
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spelling Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Mazo Lopera, Mauricio Alejandro225507a038cae9633b438fc57783c3b2Escobar Arias, Jose Antonio600193592e5d7c6087a9b8965e1127f72024-06-19T21:06:15Z2024-06-19T21:06:15Z2024https://repositorio.unal.edu.co/handle/unal/86279Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/En este trabajo de investigación, se propone el desarrollo de un modelo de regresión lineal con respuesta multivariada asociado a la clase de distribuciones normal/independiente multivariadas. El objetivo principal es lograr el modelado de cuantiles marginales bajo la presencia de datos faltantes, teniendo en cuenta la asociación entre las variables del vector de respuesta. Se emplea un enfoque Bayesiano, aprovechando las herramientas que este ofrece, como también algoritmos (que serán descritos posteriormente) para llevar a cabo el proceso de imputación y aproximación de distribuciones posteriores. La validez del modelo se evalúa mediante estudios de simulación, que confirman el desempeño satisfactorio en el proceso de estimación de los parámetros. Además, se presenta una aplicación práctica del modelo a un conjunto de datos reales, proporcionando así una validación adicional de su utilidad y aplicabilidad en contextos empíricos. (Tomado de la fuente)In this research work, we propose the development of a multivariate linear regression model associated with the class of normal/independent multivariate distributions. The primary objective is to achieve modeling of marginal quantiles in the presence of missing data, considering the association among variables in the response vector. A Bayesian approach is employed, leveraging the tools offered by this approach, including algorithms (which will be described later) for imputation and posterior distribution calculations. The model's validity is assessed through simulation studies, confirming the satisfactory performance of parameters estimation. Additionally, a practical application of the model to a real dataset is presented, providing further validation of its utility and applicability in empirical contexts.MaestríaMagíster en Ciencias - EstadísticaModelado de CuantilesEstadística.Sede Medellín94 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Ciencias - Maestría en Ciencias - EstadísticaFacultad de CienciasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasModelos log-linealesAnálisis de regresiónAnálisis multivarianteAlgoritmo de Aumento de Datos Monótonos (MDA Algorithm)distribuciónn normal/independiente multivariadadistribución log-normal/independiente multivariadamodelado de cuantilesdatos faltantesregresión lineal multivariadaMonotone Data Augmentation Algorithm (MDA Algorithm)multivariate normal/independent distributionmultivariate log-normal/independent distributionquantile modelingmissing datamultivariate linear regressionModelado de cuantiles marginales en presencia de datos faltantes mediante la clase de modelos de regresión con distribución normal/independiente multivariadaModeling of marginal quantiles in the presence of missing data using the class of regression models with normal/independent multivariate distributionTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMLaReferenciaAbanto-Valle, C. 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Statistics & Probability Letters, 54 (4), 437–447.EstudiantesInvestigadoresMaestrosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86279/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1123415188.2024.pdf1123415188.2024.pdfTesis de Maestría en Ciencias - Estadísticaapplication/pdf1301165https://repositorio.unal.edu.co/bitstream/unal/86279/2/1123415188.2024.pdfa7e625c7e545c0eea2c8c76fb19dc85fMD52THUMBNAIL1123415188.2024.pdf.jpg1123415188.2024.pdf.jpgGenerated Thumbnailimage/jpeg5179https://repositorio.unal.edu.co/bitstream/unal/86279/3/1123415188.2024.pdf.jpgcb063782430c53c491b06357615d2b05MD53unal/86279oai:repositorio.unal.edu.co:unal/862792024-08-25 23:11:42.758Repositorio Institucional Universidad Nacional de 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