Three Strategies for the Generation of Random Numbers from the Bivariate Weibull Distribution Assuming Dependence: Copula Methodologies

Autores:
Correa Ávarez, Cristian David
Jaramillo Elorza, Mario César
Bru Cordero, Osnamir Elias
Tipo de recurso:
Article of journal
Fecha de publicación:
2025
Institución:
Universidad Pedagógica y Tecnológica de Colombia
Repositorio:
RiUPTC: Repositorio Institucional UPTC
Idioma:
eng
OAI Identifier:
oai:repositorio.uptc.edu.co:001/18809
Acceso en línea:
https://doi.org/10.19053/uptc.01217488.v16.n2.2025.18956
Palabra clave:
Cópula
Dependencia
Generación
Modelos Weibull bivariados
Contorno teórico
Copula
Dependence
Generating
Bivariate weibull models
Theoretical contour
Rights
openAccess
License
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
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network_acronym_str REPOUPTC2
network_name_str RiUPTC: Repositorio Institucional UPTC
repository_id_str
dc.title.es-ES.fl_str_mv Three Strategies for the Generation of Random Numbers from the Bivariate Weibull Distribution Assuming Dependence: Copula Methodologies
title Three Strategies for the Generation of Random Numbers from the Bivariate Weibull Distribution Assuming Dependence: Copula Methodologies
spellingShingle Three Strategies for the Generation of Random Numbers from the Bivariate Weibull Distribution Assuming Dependence: Copula Methodologies
Cópula
Dependencia
Generación
Modelos Weibull bivariados
Contorno teórico
Copula
Dependence
Generating
Bivariate weibull models
Theoretical contour
title_short Three Strategies for the Generation of Random Numbers from the Bivariate Weibull Distribution Assuming Dependence: Copula Methodologies
title_full Three Strategies for the Generation of Random Numbers from the Bivariate Weibull Distribution Assuming Dependence: Copula Methodologies
title_fullStr Three Strategies for the Generation of Random Numbers from the Bivariate Weibull Distribution Assuming Dependence: Copula Methodologies
title_full_unstemmed Three Strategies for the Generation of Random Numbers from the Bivariate Weibull Distribution Assuming Dependence: Copula Methodologies
title_sort Three Strategies for the Generation of Random Numbers from the Bivariate Weibull Distribution Assuming Dependence: Copula Methodologies
dc.creator.fl_str_mv Correa Ávarez, Cristian David
Jaramillo Elorza, Mario César
Bru Cordero, Osnamir Elias
dc.contributor.author.spa.fl_str_mv Correa Ávarez, Cristian David
Jaramillo Elorza, Mario César
Bru Cordero, Osnamir Elias
dc.subject.none.fl_str_mv Cópula
Dependencia
Generación
Modelos Weibull bivariados
Contorno teórico
Copula
Dependence
Generating
Bivariate weibull models
Theoretical contour
topic Cópula
Dependencia
Generación
Modelos Weibull bivariados
Contorno teórico
Copula
Dependence
Generating
Bivariate weibull models
Theoretical contour
publishDate 2025
dc.date.accessioned.none.fl_str_mv 2025-07-25 00:00:00
dc.date.available.none.fl_str_mv 2025-07-25 00:00:00
dc.date.issued.none.fl_str_mv 2025-07-25
dc.type.spa.fl_str_mv Artículo de revista
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dc.type.local.eng.fl_str_mv Journal article
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dc.type.content.eng.fl_str_mv Text
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dc.identifier.doi.none.fl_str_mv 10.19053/uptc.01217488.v16.n2.2025.18956
dc.identifier.eissn.none.fl_str_mv 2462-7658
dc.identifier.url.none.fl_str_mv https://doi.org/10.19053/uptc.01217488.v16.n2.2025.18956
identifier_str_mv 0121-7488
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url https://doi.org/10.19053/uptc.01217488.v16.n2.2025.18956
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.references.eng.fl_str_mv J. I. McCool, “Using the Weibull Distribution: Reliability, Modeling, and Inference”, vol. 950. John Wiley & Sons, 2012.
N. L. Johnson, S. Kotz, y N. Balakrishnan, “Continuous Univariate Distributions”, Volume 2, vol. 289. John Wiley & Sons, 1995.
S. Mireh, A. Khodadadi, y F. Haghighi, “Copula-based reliability analysis of gamma degradation process and Weibull failure time”, International Journal of Quality & Reliability Management, vol. 36, no. 5, pp. 654-668, 2019.
T. Zhang y M. Xie, “Failure data analysis with extended Weibull distribution”, Communications in Statistics Simulation and Computation, vol. 36, no. 3, pp. 579-592, 2007.
N. L. Clement y R. C. Lasky, “Weibull distribution and analysis: 2019”, en 2020 Pan Pacific Microelectronics Symposium (Pan Pacific), pp. 1-5, 2020.
D. C. Montgomery, “Introduction to Statistical Quality Control”. John Wiley & Sons, 2019.
X.S.Tang, D.Q.Li,C.B.Zhou,yL.M.Zhang,“Bivariate distribution models using copulas for reliability analysis”, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, vol. 227, no. 5, pp. 499-512, 2013.
J. F. Lawless, “Statistical Models and Methods for Lifetime Data”. Wiley, Hoboken, New Jersey, 2003.
P. Hougaard, “Survival models for heterogeneous populations derived from stable distributions”, Biometrika, vol. 73, no. 2, pp. 387-396, 1986.
J. C. Lu y G. K. Bhattacharyya, “Some new constructions of bivariate Weibull models”, Annals of the Institute of Statistical Mathematics, vol. 42, no. 3, pp. 543-559, 1990.
W. Q. Meeker y L. A. Escobar, “Statistical Methods for Reliability Data”. John Wiley & Sons, New York, 1998.
M. J. Crowder, “Classical Competing Risks”. Chapman and Hall/CRC, 2001.
R. B. Nelsen, “An Introduction to Copulas”. Springer Science & Business Media, New York, 2006.
B. H. Wu, H. Michimae, y T. Emura, “Meta-analysis of individual patient data with semi-competing risks under the Weibull joint frailty-copula model”, Computational Statistics, vol. 35, pp. 1525-1552, 2020.
H.MichimaeyT.Emura,“Likelihoodinference for copula models based on left-truncated and competing risks data from field studies”, Mathematics, vol. 10, no. 13, p. 2163, 2022.
K. Wifvat, J. Kumerow, y A. Shemyakin, “Copula model selection for vehicle component failures based on warranty claims”, Risks, vol. 8, no. 2, p. 56, 2020.
J. Größer y O. Okhrin, “Copulae: An overview and recent developments”, Wiley Interdisciplinary Reviews: Computational Statistics, vol. 14, no. 3, p. e1557, 2022.
Y. Aliyu y U. Usman, “On Bivariate Nadarajah-Haghighi Distribution derived from Farlie-Gumbel-Morgenstern Copula in the Presence of Covariates”, Journal of the Nigerian Society of Physical Sciences, pp. 871-871, 2023.
S.O.Ahmed,K.S.Fatah, yS.Esa, “LocalDependencefor Bivariate Weibull Distributions Created by Archimedean Copula”, Baghdad Science Journal, vol. 18, no. 1 (Suppl.), pp. 0816-0816, 2021.
M. C. Jones, A. Noufaily, y K. Burke, “A bivariate power generalized Weibull distribution: a flexible parametric model for survival analysis”, Statistical Methods in Medical Research, vol. 29, no. 8, pp. 2295-2306, 2020.
D. N. Prabhakar Murthy, M. Xie, y R. Jiang, “Weibull Models”. John Wiley & Sons, 2004.
E. M. Almetwally, H. Z. Muhammed, y E.-S. A. El Sherpieny, “Bivariate Weibull distribution: properties and different methods of estimation”, Annals of Data Science, vol. 7, pp. 163-193, 2020.
E. W. Frees y E. A. Valdez, “Understanding relationships using copulas”, North American Actuarial Journal, vol. 2, no. 1, pp. 1-25, 1998.
M. C. Jaramillo, C. M. Lopera, E. C. Manotas, y S. Yáñez, “Generación de tiempos de falla dependientes Weibull bivariados usando cópulas”, Revista Colombiana de Estadística, vol. 31, no. 2, pp. 169-181, 2008.
E. Brechmann y U. Schepsmeier, “Cdvine: Modeling dependence with c-and d-vine copulas in R”, Journal of Statistical Software, vol. 52, no. 3, pp. 1-27, 2013.
S. Yáñez, L. A. Escobar, y N. González, “Characteristics of two competing risks models with Weibull distributed risks”, Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales, vol. 38, no. 148, pp. 298-311, 2014.
S. Shinohara, Y.-H. Lin, H. Michimae, y T. Emura, “Dynamic lifetime prediction using a Weibull-based bivariate failure time model: A meta-analysis of individual-patient data”, Communications in Statistics-Simulation and Computation, vol. 52, no. 2, pp. 349-368, 2023.
A. K. Pathak, M. Arshad, Q. J. Azhad, M. Khetan, y A. Pandey, “A novel bivariate generalized Weibull distribution with properties and applications”, American Journal of Mathematical and Management Sciences, vol. 42, no. 4, pp. 279-306, 2023.
E. S. A. El-Sherpieny, E. M. Almetwally, y H. Z. Muhammed, “Bivariate weibull-g family based on copula function: properties, bayesian and non-bayesian estimation and applications”, Statistics, Optimization & Information Computing, vol. 10, no. 3, pp. 678-709, 2022.
M. Xu, J. W. Herrmann, y E. L. Droguett, “Modeling dependent series systems with q-Weibull distribution and Clayton copula”, Applied Mathematical Modelling, vol. 94, pp. 117-138, 2021.
X. W. Huang, W. Wang, y T. Emura, “A copula-based Markov chain model for serially dependent event times with a dependent terminal event”, Japanese Journal of Statistics and Data Science, vol. 4, pp. 917-951, 2021.
N. Balakrishna y C. D. Lai, “Construction of Bivariate Distributions”. Springer, New York, 2008.
M. Franco, J. M. Vivo, y D. Kundu, “A generator of bivariate distributions: Properties, estimation, and applications”, Mathematics, vol. 8, no. 10, p. 1776, 2020.
A. W. Marshall y I. Olkin, “Families of multivariate distributions”, Journal of the American Statistical Association, vol. 83, no. 403, pp. 834-841, 1988.
M. Bekçi y M. Yilmaz, “Construction of Bivariate Distribution by Mixing Positively Dependent and Negatively Dependent Distributions”, International Journal of Statistics and Applications, vol. 9, no. 4, pp. 122-127, 2019.
H. Ü. ÜNÖZKAN y M. Yilmaz, “Construction of continuous bivariate distribution by transmuting dependent distribution”, Cumhuriyet Science Journal, vol. 40, no. 4, pp. 860-866, 2019.
L. Devroye, “Non Uniform Random Variate Generation”. Springer Verleg, New York, 1986.
C.Genest yM.Scherer, “The world of vines: An interview with Claudia Czado”, Dependence Modeling, vol. 7, no. 1, pp. 169-180, 2019.
M.E.Johnson, “Multivariate Statistical Simulation”. John Wiley & Sons, New York, 1987.
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spelling Correa Ávarez, Cristian DavidJaramillo Elorza, Mario CésarBru Cordero, Osnamir Elias2025-07-25 00:00:002025-07-25 00:00:002025-07-250121-748810.19053/uptc.01217488.v16.n2.2025.189562462-7658https://doi.org/10.19053/uptc.01217488.v16.n2.2025.18956application/pdfengUniversidad Pedagógica y Tecnológica de Colombiahttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.esinfo:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.http://purl.org/coar/access_right/c_abf2https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/18956CópulaDependenciaGeneraciónModelos Weibull bivariadosContorno teóricoCopulaDependenceGeneratingBivariate weibull modelsTheoretical contourThree Strategies for the Generation of Random Numbers from the Bivariate Weibull Distribution Assuming Dependence: Copula MethodologiesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/coar/version/c_970fb48d4fbd8a85J. I. McCool, “Using the Weibull Distribution: Reliability, Modeling, and Inference”, vol. 950. John Wiley & Sons, 2012.N. L. Johnson, S. Kotz, y N. Balakrishnan, “Continuous Univariate Distributions”, Volume 2, vol. 289. John Wiley & Sons, 1995.S. Mireh, A. Khodadadi, y F. Haghighi, “Copula-based reliability analysis of gamma degradation process and Weibull failure time”, International Journal of Quality & Reliability Management, vol. 36, no. 5, pp. 654-668, 2019.T. Zhang y M. Xie, “Failure data analysis with extended Weibull distribution”, Communications in Statistics Simulation and Computation, vol. 36, no. 3, pp. 579-592, 2007.N. L. Clement y R. C. Lasky, “Weibull distribution and analysis: 2019”, en 2020 Pan Pacific Microelectronics Symposium (Pan Pacific), pp. 1-5, 2020.D. C. Montgomery, “Introduction to Statistical Quality Control”. John Wiley & Sons, 2019.X.S.Tang, D.Q.Li,C.B.Zhou,yL.M.Zhang,“Bivariate distribution models using copulas for reliability analysis”, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, vol. 227, no. 5, pp. 499-512, 2013.J. F. Lawless, “Statistical Models and Methods for Lifetime Data”. Wiley, Hoboken, New Jersey, 2003.P. Hougaard, “Survival models for heterogeneous populations derived from stable distributions”, Biometrika, vol. 73, no. 2, pp. 387-396, 1986.J. C. Lu y G. K. Bhattacharyya, “Some new constructions of bivariate Weibull models”, Annals of the Institute of Statistical Mathematics, vol. 42, no. 3, pp. 543-559, 1990.W. Q. Meeker y L. A. Escobar, “Statistical Methods for Reliability Data”. John Wiley & Sons, New York, 1998.M. J. Crowder, “Classical Competing Risks”. Chapman and Hall/CRC, 2001.R. B. Nelsen, “An Introduction to Copulas”. Springer Science & Business Media, New York, 2006.B. H. Wu, H. Michimae, y T. Emura, “Meta-analysis of individual patient data with semi-competing risks under the Weibull joint frailty-copula model”, Computational Statistics, vol. 35, pp. 1525-1552, 2020.H.MichimaeyT.Emura,“Likelihoodinference for copula models based on left-truncated and competing risks data from field studies”, Mathematics, vol. 10, no. 13, p. 2163, 2022.K. Wifvat, J. Kumerow, y A. Shemyakin, “Copula model selection for vehicle component failures based on warranty claims”, Risks, vol. 8, no. 2, p. 56, 2020.J. Größer y O. Okhrin, “Copulae: An overview and recent developments”, Wiley Interdisciplinary Reviews: Computational Statistics, vol. 14, no. 3, p. e1557, 2022.Y. Aliyu y U. Usman, “On Bivariate Nadarajah-Haghighi Distribution derived from Farlie-Gumbel-Morgenstern Copula in the Presence of Covariates”, Journal of the Nigerian Society of Physical Sciences, pp. 871-871, 2023.S.O.Ahmed,K.S.Fatah, yS.Esa, “LocalDependencefor Bivariate Weibull Distributions Created by Archimedean Copula”, Baghdad Science Journal, vol. 18, no. 1 (Suppl.), pp. 0816-0816, 2021.M. C. Jones, A. Noufaily, y K. Burke, “A bivariate power generalized Weibull distribution: a flexible parametric model for survival analysis”, Statistical Methods in Medical Research, vol. 29, no. 8, pp. 2295-2306, 2020.D. N. Prabhakar Murthy, M. Xie, y R. Jiang, “Weibull Models”. John Wiley & Sons, 2004.E. M. Almetwally, H. Z. Muhammed, y E.-S. A. El Sherpieny, “Bivariate Weibull distribution: properties and different methods of estimation”, Annals of Data Science, vol. 7, pp. 163-193, 2020.E. W. Frees y E. A. Valdez, “Understanding relationships using copulas”, North American Actuarial Journal, vol. 2, no. 1, pp. 1-25, 1998.M. C. Jaramillo, C. M. Lopera, E. C. Manotas, y S. Yáñez, “Generación de tiempos de falla dependientes Weibull bivariados usando cópulas”, Revista Colombiana de Estadística, vol. 31, no. 2, pp. 169-181, 2008.E. Brechmann y U. Schepsmeier, “Cdvine: Modeling dependence with c-and d-vine copulas in R”, Journal of Statistical Software, vol. 52, no. 3, pp. 1-27, 2013.S. Yáñez, L. A. Escobar, y N. González, “Characteristics of two competing risks models with Weibull distributed risks”, Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales, vol. 38, no. 148, pp. 298-311, 2014.S. Shinohara, Y.-H. Lin, H. Michimae, y T. Emura, “Dynamic lifetime prediction using a Weibull-based bivariate failure time model: A meta-analysis of individual-patient data”, Communications in Statistics-Simulation and Computation, vol. 52, no. 2, pp. 349-368, 2023.A. K. Pathak, M. Arshad, Q. J. Azhad, M. Khetan, y A. Pandey, “A novel bivariate generalized Weibull distribution with properties and applications”, American Journal of Mathematical and Management Sciences, vol. 42, no. 4, pp. 279-306, 2023.E. S. A. El-Sherpieny, E. M. Almetwally, y H. Z. Muhammed, “Bivariate weibull-g family based on copula function: properties, bayesian and non-bayesian estimation and applications”, Statistics, Optimization & Information Computing, vol. 10, no. 3, pp. 678-709, 2022.M. Xu, J. W. Herrmann, y E. L. Droguett, “Modeling dependent series systems with q-Weibull distribution and Clayton copula”, Applied Mathematical Modelling, vol. 94, pp. 117-138, 2021.X. W. Huang, W. Wang, y T. Emura, “A copula-based Markov chain model for serially dependent event times with a dependent terminal event”, Japanese Journal of Statistics and Data Science, vol. 4, pp. 917-951, 2021.N. Balakrishna y C. D. Lai, “Construction of Bivariate Distributions”. Springer, New York, 2008.M. Franco, J. M. Vivo, y D. Kundu, “A generator of bivariate distributions: Properties, estimation, and applications”, Mathematics, vol. 8, no. 10, p. 1776, 2020.A. W. Marshall y I. Olkin, “Families of multivariate distributions”, Journal of the American Statistical Association, vol. 83, no. 403, pp. 834-841, 1988.M. Bekçi y M. Yilmaz, “Construction of Bivariate Distribution by Mixing Positively Dependent and Negatively Dependent Distributions”, International Journal of Statistics and Applications, vol. 9, no. 4, pp. 122-127, 2019.H. Ü. ÜNÖZKAN y M. Yilmaz, “Construction of continuous bivariate distribution by transmuting dependent distribution”, Cumhuriyet Science Journal, vol. 40, no. 4, pp. 860-866, 2019.L. Devroye, “Non Uniform Random Variate Generation”. Springer Verleg, New York, 1986.C.Genest yM.Scherer, “The world of vines: An interview with Claudia Czado”, Dependence Modeling, vol. 7, no. 1, pp. 169-180, 2019.M.E.Johnson, “Multivariate Statistical Simulation”. John Wiley & Sons, New York, 1987.160215116Ciencia en Desarrollohttps://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/download/18956/16621Núm. 2 , Año 2025 : Vol. 16 Núm. 2 (2025): Vol. 16 Núm. 2 (2025): Vol 16, Núm.2 (2025): Julio-DiciembrePublicationOREORE.xmltext/xml2873https://repositorio.uptc.edu.co/bitstreams/eb6ee0ce-51f9-4266-827a-9a83e4574ba6/download0cbf44395be0e76363a38b9816f79fbbMD51falseAnonymousREAD001/18809oai:repositorio.uptc.edu.co:001/188092026-10-30 07:02:51.193https://creativecommons.org/licenses/by-nc-nd/4.0/deed.esmetadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCbdigital@metabiblioteca.com

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