Inference in Multiple Linear Regression Model with Generalized Secant Hyperbolic Distribution Errors

We study multiple linear regression model under non-normally distributed random error by considering the family of generalized secant hyperbolic distributions. We derive the estimators of model parameters by using modified maximum likelihood methodology and explore the properties of the modified max...

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Autores:
Burbano Moreno, Álvaro Alexander
Melo-Martinez, Oscar Orlando
Qamarul Islam, M
Tipo de recurso:
Fecha de publicación:
2021
Institución:
Universidad EAFIT
Repositorio:
Repositorio EAFIT
Idioma:
eng
OAI Identifier:
oai:repository.eafit.edu.co:10784/30404
Acceso en línea:
http://hdl.handle.net/10784/30404
Palabra clave:
Maximum likelihood
Modified maximum likelihood
Least square
Generalized Secant Hyperbolic distribution
Robustness
Hypothesis testing
Rights
License
Copyright © 2021 Álvaro Alexander Burbano Moreno, Oscar Orlando Melo-Martinez, M Qamarul Islam
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spelling Medellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees2021-05-122021-10-05T16:59:46Z2021-05-122021-10-05T16:59:46Z1794-91652256-4314http://hdl.handle.net/10784/30404We study multiple linear regression model under non-normally distributed random error by considering the family of generalized secant hyperbolic distributions. We derive the estimators of model parameters by using modified maximum likelihood methodology and explore the properties of the modified maximum likelihood estimators so obtained. We show that the proposed estimators are more efficient and robust than the commonly used least square estimators. We also develop the relevant test of hypothesis procedures and compared the performance of such tests vis-a-vis the classical tests that are based upon the least square approach.application/pdfengUniversidad EAFIThttps://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/6516/5159https://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/6516/5159Copyright © 2021 Álvaro Alexander Burbano Moreno, Oscar Orlando Melo-Martinez, M Qamarul IslamAcceso abiertohttp://purl.org/coar/access_right/c_abf2Ingeniería y Ciencia, Vol 17, No. 33 (2021)Inference in Multiple Linear Regression Model with Generalized Secant Hyperbolic Distribution Errorsinfo:eu-repo/semantics/articlearticleinfo:eu-repo/semantics/publishedVersionpublishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Maximum likelihoodModified maximum likelihoodLeast squareGeneralized Secant Hyperbolic distributionRobustnessHypothesis testingBurbano Moreno, Álvaro AlexanderMelo-Martinez, Oscar OrlandoQamarul Islam, MUniversidade Federal Minas GeraisUniversidad Nacional de ColombiaMiddle East Technical UniversityIngeniería y Ciencia17334570ORIGINALInference in Multiple Linear.pdfInference in Multiple Linear.pdfTexto completo PDFapplication/pdf594956https://repository.eafit.edu.co/bitstreams/4e433a90-cfae-4cf7-9b09-dca96cba4de2/download9a0b3f9597427c5032401ab639b47925MD51articulo.htmlarticulo.htmlTexto completo HTMLtext/html361https://repository.eafit.edu.co/bitstreams/90b8c56a-10a1-4918-954b-588784ae2dc8/download394174087fb5c7dfe805d5fad47316b6MD52THUMBNAILminaitura-ig_Mesa de trabajo 1.jpgminaitura-ig_Mesa de trabajo 1.jpgimage/jpeg265796https://repository.eafit.edu.co/bitstreams/fc2b278c-6cd0-466e-a19e-3f19606dec5b/downloadda9b21a5c7e00c7f1127cef8e97035e0MD5310784/30404oai:repository.eafit.edu.co:10784/304042022-05-02 14:55:51.471open.accesshttps://repository.eafit.edu.coRepositorio Institucional Universidad EAFITrepositorio@eafit.edu.co
dc.title.eng.fl_str_mv Inference in Multiple Linear Regression Model with Generalized Secant Hyperbolic Distribution Errors
title Inference in Multiple Linear Regression Model with Generalized Secant Hyperbolic Distribution Errors
spellingShingle Inference in Multiple Linear Regression Model with Generalized Secant Hyperbolic Distribution Errors
Maximum likelihood
Modified maximum likelihood
Least square
Generalized Secant Hyperbolic distribution
Robustness
Hypothesis testing
title_short Inference in Multiple Linear Regression Model with Generalized Secant Hyperbolic Distribution Errors
title_full Inference in Multiple Linear Regression Model with Generalized Secant Hyperbolic Distribution Errors
title_fullStr Inference in Multiple Linear Regression Model with Generalized Secant Hyperbolic Distribution Errors
title_full_unstemmed Inference in Multiple Linear Regression Model with Generalized Secant Hyperbolic Distribution Errors
title_sort Inference in Multiple Linear Regression Model with Generalized Secant Hyperbolic Distribution Errors
dc.creator.fl_str_mv Burbano Moreno, Álvaro Alexander
Melo-Martinez, Oscar Orlando
Qamarul Islam, M
dc.contributor.author.none.fl_str_mv Burbano Moreno, Álvaro Alexander
Melo-Martinez, Oscar Orlando
Qamarul Islam, M
dc.contributor.affiliation.none.fl_str_mv Universidade Federal Minas Gerais
Universidad Nacional de Colombia
Middle East Technical University
dc.subject.keyword.eng.fl_str_mv Maximum likelihood
Modified maximum likelihood
Least square
Generalized Secant Hyperbolic distribution
Robustness
Hypothesis testing
topic Maximum likelihood
Modified maximum likelihood
Least square
Generalized Secant Hyperbolic distribution
Robustness
Hypothesis testing
description We study multiple linear regression model under non-normally distributed random error by considering the family of generalized secant hyperbolic distributions. We derive the estimators of model parameters by using modified maximum likelihood methodology and explore the properties of the modified maximum likelihood estimators so obtained. We show that the proposed estimators are more efficient and robust than the commonly used least square estimators. We also develop the relevant test of hypothesis procedures and compared the performance of such tests vis-a-vis the classical tests that are based upon the least square approach.
publishDate 2021
dc.date.available.none.fl_str_mv 2021-10-05T16:59:46Z
dc.date.issued.none.fl_str_mv 2021-05-12
dc.date.accessioned.none.fl_str_mv 2021-10-05T16:59:46Z
dc.date.none.fl_str_mv 2021-05-12
dc.type.eng.fl_str_mv info:eu-repo/semantics/article
article
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2256-4314
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10784/30404
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url http://hdl.handle.net/10784/30404
dc.language.iso.none.fl_str_mv eng
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dc.relation.uri.none.fl_str_mv https://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/6516/5159
dc.rights.eng.fl_str_mv Copyright © 2021 Álvaro Alexander Burbano Moreno, Oscar Orlando Melo-Martinez, M Qamarul Islam
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.local.spa.fl_str_mv Acceso abierto
rights_invalid_str_mv Copyright © 2021 Álvaro Alexander Burbano Moreno, Oscar Orlando Melo-Martinez, M Qamarul Islam
Acceso abierto
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dc.format.none.fl_str_mv application/pdf
dc.coverage.spatial.none.fl_str_mv Medellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees
dc.publisher.spa.fl_str_mv Universidad EAFIT
dc.source.spa.fl_str_mv Ingeniería y Ciencia, Vol 17, No. 33 (2021)
institution Universidad EAFIT
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