Intervalos de confianza para la confiabilidad de sistemas coherentes no-reparables con estructura dependiente en la familia Weibull.

diagramas, tablas

Autores:: Bru Cordero, Osnamir Elias

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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dc.title.spa.fl_str_mv	Intervalos de confianza para la confiabilidad de sistemas coherentes no-reparables con estructura dependiente en la familia Weibull.
dc.title.translated.eng.fl_str_mv	Confidence interval for systems reliability of coherent non-repairable with dependent structure in the Weibull family.
title	Intervalos de confianza para la confiabilidad de sistemas coherentes no-reparables con estructura dependiente en la familia Weibull.
spellingShingle	Intervalos de confianza para la confiabilidad de sistemas coherentes no-reparables con estructura dependiente en la familia Weibull. 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas 310 - Colecciones de estadística general Confiabilidad (Ingeniería) - Métodos estadísticos Reliability (engineering) - statistical methods Sistemas coherentes Componentes dependientes Cópula Fragilidad Coherent systems Dependent components Copula Frailty Reliability Coherent systems Dependent components Copula Frailty
title_short	Intervalos de confianza para la confiabilidad de sistemas coherentes no-reparables con estructura dependiente en la familia Weibull.
title_full	Intervalos de confianza para la confiabilidad de sistemas coherentes no-reparables con estructura dependiente en la familia Weibull.
title_fullStr	Intervalos de confianza para la confiabilidad de sistemas coherentes no-reparables con estructura dependiente en la familia Weibull.
title_full_unstemmed	Intervalos de confianza para la confiabilidad de sistemas coherentes no-reparables con estructura dependiente en la familia Weibull.
title_sort	Intervalos de confianza para la confiabilidad de sistemas coherentes no-reparables con estructura dependiente en la familia Weibull.
dc.creator.fl_str_mv	Bru Cordero, Osnamir Elias
dc.contributor.advisor.none.fl_str_mv	Jaramillo Elorza, Mario César
dc.contributor.author.none.fl_str_mv	Bru Cordero, Osnamir Elias
dc.contributor.researchgroup.spa.fl_str_mv	Confiabilidad Industrial
dc.subject.ddc.spa.fl_str_mv	510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas 310 - Colecciones de estadística general
topic	510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas 310 - Colecciones de estadística general Confiabilidad (Ingeniería) - Métodos estadísticos Reliability (engineering) - statistical methods Sistemas coherentes Componentes dependientes Cópula Fragilidad Coherent systems Dependent components Copula Frailty Reliability Coherent systems Dependent components Copula Frailty
dc.subject.armarc.none.fl_str_mv	Confiabilidad (Ingeniería) - Métodos estadísticos
dc.subject.lemb.none.fl_str_mv	Reliability (engineering) - statistical methods
dc.subject.proposal.spa.fl_str_mv	Sistemas coherentes Componentes dependientes Cópula Fragilidad
dc.subject.proposal.eng.fl_str_mv	Coherent systems Dependent components Copula Frailty Reliability Coherent systems Dependent components Copula Frailty
description	diagramas, tablas
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-10-12T15:57:58Z
dc.date.available.none.fl_str_mv	2021-10-12T15:57:58Z
dc.date.issued.none.fl_str_mv	2021-10-11
dc.type.spa.fl_str_mv	Trabajo de grado - Doctorado
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dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/80511
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
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identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
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dc.relation.references.spa.fl_str_mv	Aalen, O., Borgan, O., y Gjessing, H. (2008). Survival and Event History Analysis: A Process Point of View. Springer Science & Business Media Aven, T., y Jensen, U. (2013). Stochastic Models in Reliability. Springer. Barlow, R., y Proschan, F. (1975). Statistical Theory of Reliability and Life Testing: Probability Models. John Wiley & Sons. Barlow, R., y Proschan, F. (1981). Statistical Theory of Reliability and Life Testing. To Begin With. Bemis, B. M., Bain, L. J., y Higgins, J. J. (1972). Estimation and hypothesis testing for the parameters of a bivariate exponential distribution. Journal of the American Statistical Association, 67 , 927-929. Belaghi, R. A., y Asl, M. N. (2019). Estimation based on progressively type-I hybrid censored data from the Burr XII distribution. Statistical Papers, 60 , 761-803. Bru, O. E. C., y Jaramillo, M. C. E. (2019). Uso del método Combinación de Riesgos para estimar la función de supervivencia en presencia de riesgos competitivos dependientes: Un estudio de simulación. Ciencia en Desarrollo, 10 , 67-77. Burr, I. W. (1942). Cumulative frequency functions. The Annals of Mathematical Statistics, 13 , 215-232. Carriere, J. F. (1995). Removing cancer when it is correlated with other causes of death. Biometrical Journal, 37 , 339-350. Chan, V., y Meeker, W. Q. (1999). A failure-time model for infant-mortality and wearout failure modes. IEEE Transactions on Reliability, 48 , 377-387. Crowder, M. J. (2001). Classical Competing Risks. New York: Chapman & Hall/CRC. Crowder, M. J. (2011). Multivariate Survival Analysis and Competing Risks. New York: Chapman & Hall/CRC. David, H. A., y Moeschberger, M. L. (1978). The Theory of Competing Risks. C. Griffn. Duchateau, L., y Janssen, P. (2007). The Frailty Model. Springer Science & Business Media. Emura, T., Nakatochi, M., Murotani, K., y Rondeau, V. (2017). A joint frailty-copula model between tumour progression and death for meta-analysis. Statistical Methods in Medical Research, 26 , 2649-2666. Epstein, B. (1958). The Exponential Distribution and its Role in Life Testing. Wayne State University Detroit MI. Esary, J. D. (1957). A Stochastic Theory of Accident Survival and Fatality. University of California, Berkeley. Escarela, G., y Carriere, J. (2003). Fitting competing risks with an assumed copula. Statis- tical Methods in Medical Research, 12 , 33-349. Escobar, L. A., Villa, E. R., y Ya~nez, S. (2003). Confi abilidad: Historia, estado del arte y desafíos futuros. Dyna, 70 , 5-21. Feizjavadian, S., y Hashemi, R. (2015). Analysis of dependent competing risks in the presence of progressive hybrid censoring using Marshall{Olkin bivariate Weibull distribution. Computational Statistics & Data Analysis, 82 , 19-34. Fréchet, M. (1960). Sur les tableaux dont les marges et des bornes sont donn ees. Revue de l'Institut International de Statistique, 10-32. Gandy, A. (2005). Effects of uncertainties in components on the survival of complex systems with given dependencies. Modern Statistical and Mathematical Methods in Reliability. World Scienti c, New Jersey, 177-189. Gaver, D. (1963). Random hazard in reliability problems. Technometrics, 5 , 211-226. Gumbel, E. J. (1960). Bivariate exponential distributions. Journal of the American Statistical Association, 55 , 698-707. Hong, Y. (2013). On computing the distribution function for the Poisson binomial distribution. Computational Statistics and Data Analysis, 59 , 41-51. Hong, Y., y Meeker, W. Q. (2014). Con dence interval procedures for system reliability and applications to competing risks models. Lifetime Data Analysis, 20 , 161-184. Hong, Y., Meeker, W. Q., y Escobar, L. A. (2008). Avoiding problems with normal approximation confi dence intervals for probabilities. Technometrics, 50 , 64-68. Hougaard, P. (2012). Analysis of Multivariate Survival Data. Springer Science & Business Media. Ibrahim, J. G., Chen, M.-H., y Sinha, D. (2014). Bayesian Survival Analysis. John Wiley & Sons: Statistics Reference Online. Joe, H. (1997). Multivariate Models and Multivariate Dependence Concepts. CRC Press. Kotz, S., Balakrishnan, N., y Johnson, N. L. (2004). Continuous Multivariate Distributions, Volume 1: Models and Applications. John Wiley & Sons. Kundu, D. (2007). On hybrid censored Weibull distribution. Journal of Statistical Planning and Inference, 137 , 2127-2142. Kundu, D., y Basu, S. (2000). Analysis of incomplete data in presence of competing risks. Journal of Statistical Planning and Inference, 87 , 221-239. Kundu, D., y Dey, A. K. (2009). Estimating the parameters of the Marshall{Olkin bivariate Weibull distribution by EM algorithm. Computational Statistics & Data Analysis, 53 , 956-965. Lai, C.-D., Dong Lin, G., Govindaraju, K., y Pirikahu, S. (2017). A simulation study on the correlation structure of Marshall-Olkin bivariate Weibull distribution. Journal of Statistical Computation and Simulation, 87 , 156-170. Lawless, J. F. (2003). Statistics in reliability. Journal of the American Statistical Association, 95 , 989-992. Lawless, J. F. (2011). Statistical Models and Methods for Lifetime Data. John Wiley & Sons - Interscience. Lehmann, E., y Casella, G. (1998). Theory of Point Estimation, Second Edition. Springer. Li, H. (2008). Tail dependence comparison of survival Marshall-Olkin copulas. Methodology and Computing in Applied Probability, 10 , 39-54. Li, X., y Pellerey, F. (2011). Generalized Marshall-Olkin distributions and related bivariate aging properties. Journal of Multivariate Analysis, 102 , 1399-1409. Lin, H., y Zelterman, D. (2002). Modeling Survival Data: Extending the Cox Model. Taylor & Francis. Liu, X. (2012). Planning of accelerated life tests with dependent failure modes based on a gamma frailty model. Technometrics, 54 , 398-409. Lu, J.-C., y Bhattacharyya, G. K. (1990). Some new constructions of bivariate Weibull models. Annals of the Institute of Statistical Mathematics, 42 , 543-559. Marshall, A. W., y Olkin, I. (1967a). A generalized bivariate exponential distribution. Journal of Applied Probability, 4 , 291-302. Marshall, A. W., y Olkin, I. (1967b). A multivariate exponential distribution. Journal of the American Statistical Association, 62 , 30-44. McNeil, A. J., Frey, R., y Embrechts, P. (2015). Quantitative Risk Management: Concepts, Techniques and Tools-Revised Edition. Princeton University Press. Meeker, W. Q., y Escobar, L. A. (1998). Statistical Methods for Reliability Data. New York: John Wiley & Sons. Meeker, W. Q., Hahn, G. J., y Escobar, L. A. (2017). Statistical Intervals: A Guide for Practitioners and Researchers. John Wiley & Sons. Mirhosseini, S. M., Amini, M., Kundu, D., y Dolati, A. (2015). On a new absolutely continuous bivariate generalized exponential distribution. Statistical Methods & Applications, 24 , 61-83. Miyawaka, M. (1982). Statistical analysis of incomplete data in competing risks model. J. Japanese Society. Quantity Control , 12 , 49-52. Miyawaka, M. (1984). Analysis of incomplete data in competing risks model. IEEE Transactions on Reliability, 33 , 293-296. Mokhtari, E. B., Rad, A. H., y Yousefzadeh, F. (2011). Inference for Weibull distribution based on progressively type-II hybrid censored data. Journal of Statistical Planning and Inference, 141 , 2824-2838. Morgenstern, D. (1956). Einfache beispiele zweidimensionaler verteilungen. Mitteilingsblatt fur Mathematische Statistik , 8 , 234-235. Muliere, P., y Scarsini, M. (1987). Characterization of a Marshall-Olkin type class of distributions. Annals of the Institute of Statistical Mathematics, 39 , 429-441. Muller, A., y Stoyan, D. (2002). Comparison Methods for Stochastic Models and Risks. John Wiley & Sons. Murthy, D. P., Xie, M., y Jiang, R. (2004). Weibull Models. John Wiley & Sons. Navarro, J., y Durante, F. (2017). Copula-based representations for the reliability of the residual lifetimes of coherent systems with dependent components. Journal of Multivariate Analysis, 158 , 87-102. Nelsen, R. B. (2006). An Introduction to Copulas. Springer. Ota, S., y Kimura, M. (2017). A statistical dependent failure detection method for n-component parallel systems. Reliability Engineering & System Safety, 167 , 376-382. Pareek, B., Kundu, D., y Kumar, S. (2009). On progressively censored competing risks data for Weibull distributions. Computational Statistics & Data Analysis, 53 , 4083-4094. Pascual, F. G., y Gast, C. (2010). Probability plotting with independent competing risks. En Advances in Degradation Modeling (pp. 397-414.). Springer. Pawitan, Y. (2001). In All Likelihood: Statistical Modelling and Inference Using Likelihood. Oxford University Press. Paz-Sabogal, M. C., Yañez-Canal, S., y Lopera-Gómez, C. M. (2014). Comparative study of the dependence effect on competing risks models with three modes of failure via estimators copula based. Ingeniería y Competitividad , 16 , 169-183. Pintilie, M. (2006). Competing Risks: A Practical Perspective. John Wiley & Sons. Plackett, R. L. (1965). A class of bivariate distributions. Journal of the American Statistical Association, 60 , 516-522. R Core Team. (2020). R: A Language and Environment for Statistical Computing [Manual de software informático]. Vienna, Austria. Descargado de https://www.R-project.org/ Singpurwalla, N. D. (2006). Reliability and Risk: A Bayesian Perspective. John Wiley & Sons. Tableman, M., y Kim, J. S. (2003). Survival Analysis Using S: Analysis of Time-to-Event Data. CRC press. Tsiatis, A. (1975). A nonidenti ability aspect of the problem of competing risks. Proceedings of the National Academy of Sciences, 72 , 20-22. Vallejos, C. A., y Steel, M. F. (2017). Incorporating unobserved heterogeneity in Weibull survival models: A Bayesian approach. Econometrics and Statistics, 3 , 73-88. Wang, Y.-C., Emura, T., Fan, T.-H., Lo, S. M., y Wilke, R. A. (2020). Likelihood-based inference for a frailty-copula model based on competing risks failure time data. Quality and Reliability Engineering International, 36 , 1622-1638. Watkins, A. J. (1999). An algorithm for maximum likelihood estimation in the three parameter Burr XII distribution. Computational Statistics & Data Analysis, 32 , 19-27. Wienke, A. (2010). Frailty Models in Survival Analysis. CRC press. Wu, M., Shi, Y., y Zhang, C. (2017). Statistical analysis of dependent competing risks model in accelerated life testing under progressively hybrid censoring using copula function. Communications in Statistics-Simulation and Computation, 46 , 4004-4017. Yañez, S., Brango, H., Jaramillo, M. C., y Lopera, C. M. (2011). Comparación entre riesgos competitivos vía el estimador cópula-gráfi co. Revista Colombiana de Estadística, 34 , 231-248. Yañez, S., Escobar, L. A., y González, N. (2014). Characteristics of two competing risks models with Weibull distributed risks. Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales, 38 , 298-311. Zheng, M., y Klein, J. P. (1995). Estimates of marginal survival for dependent competing risks based on an assumed copula. Biometrika, 82 , 127-138.
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Ciencias - Doctorado en Ciencias - Estadística
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dc.publisher.place.spa.fl_str_mv	Medellín, Colombia
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Jaramillo Elorza, Mario César5006ce3f61bb4497c0d6a6375ae9613aBru Cordero, Osnamir Elias36601df14d6c3d6c03acd4e1700708c4Confiabilidad Industrial2021-10-12T15:57:58Z2021-10-12T15:57:58Z2021-10-11https://repositorio.unal.edu.co/handle/unal/80511Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/diagramas, tablasEn este trabajo el objetivo central es calcular intervalos de confianza para la confiabilidad de un sistema con sólo dos componentes, cuyos tiempos de vida son dependientes. Para estimar la confiabilidad del sistema teniendo en cuenta la dependencia entre los tiempos de vida del sistema coherente no reparable, se utiliza un modelo cópula Gumbel, para distribuciones de la familia de log-localización y escala; en el mismo contexto se chequean algunos resultados ya existentes bajo el escenario donde los tiempos son independientes, el cual para nuestro trabajo es un caso particular. Estas situaciones abordadas en nuestro estudio, son validadas mediante estimación de probabilidades de cobertura, para tres métodos; verosimilitud, transformación logit y ye en dos modelos de interés, modelo tradicional para riesgos competitivos con marginales Exponencial, Weibull y un modelo bivariado Marshall-Olkin Exponencial y Weibull. Se examina el comportamiento de los intervalos bajo la hipótesis de dependencia entre los tiempos de falla, y se observa que para tamaños muestrales pequeños se presenta un pequeño ruido en los intervalos, el cual fue corregido mediante una propuesta (y_e). Al incluir el concepto de fragilidad en un sistema en serie con marginales Weibull, la cual hace referencia a la variabilidad entre los tiempos de cada una de las unidades y con la propuesta se nota un mejor comportamiento de los intervalos de confianza para dicha estimación en muestras pequeñas y por supuesto para muestras grandes. (Texto tomado de la fuente)In this work, the main objective is to compute con_dence intervals for the reliability of a system with only two components, whose life times are dependent. To estimate the reliability of the system taking into account the dependence between the lifetimes of the coherent non-repairable system, a Gumbel copula model is used, for distributions of the log-location and scale family; In the same context, some existing results are checked under the scenario where the times are independent, which for our work is a particular case. These situations addressed in our study are validated by estimating coverage probabilities for three methods; likelihood, logit transformation and (y_e) in two models of interest, traditional model for competitive risks with marginal Exponential, Weibull and a Marshall-Olkin Exponential and Weibull bivariate model. The behavior of the intervals is examined under the hypothesis of dependence between the failure times, and it is observed that for small sample sizes there is a small noise in the intervals, which was corrected by a proposal (y_e). By including the concept of frailty in a serial system with Weibull marginals, which refers to the variability between the times of each of the units and with the proposal, a better behavior of the con_dence intervals for small and large samples.DoctoradoDoctor en Ciencias - EstadísticaConfiabilidadxviii, 104 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Ciencias - Doctorado en Ciencias - EstadísticaEscuela de estadísticaFacultad de CienciasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas310 - Colecciones de estadística generalConfiabilidad (Ingeniería) - Métodos estadísticosReliability (engineering) - statistical methodsSistemas coherentesComponentes dependientesCópulaFragilidadCoherent systemsDependent componentsCopulaFrailtyReliabilityCoherent systemsDependent componentsCopulaFrailtyIntervalos de confianza para la confiabilidad de sistemas coherentes no-reparables con estructura dependiente en la familia Weibull.Confidence interval for systems reliability of coherent non-repairable with dependent structure in the Weibull family.Trabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDAalen, O., Borgan, O., y Gjessing, H. (2008). Survival and Event History Analysis: A Process Point of View. Springer Science & Business MediaAven, T., y Jensen, U. (2013). Stochastic Models in Reliability. Springer.Barlow, R., y Proschan, F. (1975). Statistical Theory of Reliability and Life Testing: Probability Models. John Wiley & Sons.Barlow, R., y Proschan, F. (1981). Statistical Theory of Reliability and Life Testing. To Begin With.Bemis, B. M., Bain, L. J., y Higgins, J. J. (1972). Estimation and hypothesis testing for the parameters of a bivariate exponential distribution. Journal of the American Statistical Association, 67 , 927-929.Belaghi, R. A., y Asl, M. N. (2019). Estimation based on progressively type-I hybrid censored data from the Burr XII distribution. Statistical Papers, 60 , 761-803.Bru, O. E. C., y Jaramillo, M. C. E. (2019). Uso del método Combinación de Riesgos para estimar la función de supervivencia en presencia de riesgos competitivos dependientes: Un estudio de simulación. Ciencia en Desarrollo, 10 , 67-77.Burr, I. W. (1942). Cumulative frequency functions. The Annals of Mathematical Statistics, 13 , 215-232.Carriere, J. F. (1995). Removing cancer when it is correlated with other causes of death. Biometrical Journal, 37 , 339-350.Chan, V., y Meeker, W. Q. (1999). A failure-time model for infant-mortality and wearout failure modes. IEEE Transactions on Reliability, 48 , 377-387.Crowder, M. J. (2001). Classical Competing Risks. New York: Chapman & Hall/CRC.Crowder, M. J. (2011). Multivariate Survival Analysis and Competing Risks. New York: Chapman & Hall/CRC.David, H. A., y Moeschberger, M. L. (1978). The Theory of Competing Risks. C. Griffn.Duchateau, L., y Janssen, P. (2007). The Frailty Model. Springer Science & Business Media.Emura, T., Nakatochi, M., Murotani, K., y Rondeau, V. (2017). A joint frailty-copula model between tumour progression and death for meta-analysis. Statistical Methods in Medical Research, 26 , 2649-2666.Epstein, B. (1958). The Exponential Distribution and its Role in Life Testing. Wayne State University Detroit MI.Esary, J. D. (1957). A Stochastic Theory of Accident Survival and Fatality. University of California, Berkeley.Escarela, G., y Carriere, J. (2003). Fitting competing risks with an assumed copula. Statis- tical Methods in Medical Research, 12 , 33-349.Escobar, L. A., Villa, E. R., y Ya~nez, S. (2003). Confi abilidad: Historia, estado del arte y desafíos futuros. Dyna, 70 , 5-21.Feizjavadian, S., y Hashemi, R. (2015). Analysis of dependent competing risks in the presence of progressive hybrid censoring using Marshall{Olkin bivariate Weibull distribution. Computational Statistics & Data Analysis, 82 , 19-34.Fréchet, M. (1960). Sur les tableaux dont les marges et des bornes sont donn ees. Revue de l'Institut International de Statistique, 10-32.Gandy, A. (2005). Effects of uncertainties in components on the survival of complex systems with given dependencies. Modern Statistical and Mathematical Methods in Reliability. World Scienti c, New Jersey, 177-189.Gaver, D. (1963). Random hazard in reliability problems. Technometrics, 5 , 211-226.Gumbel, E. J. (1960). Bivariate exponential distributions. Journal of the American Statistical Association, 55 , 698-707.Hong, Y. (2013). On computing the distribution function for the Poisson binomial distribution. Computational Statistics and Data Analysis, 59 , 41-51.Hong, Y., y Meeker, W. Q. (2014). Con dence interval procedures for system reliability and applications to competing risks models. Lifetime Data Analysis, 20 , 161-184.Hong, Y., Meeker, W. Q., y Escobar, L. A. (2008). Avoiding problems with normal approximation confi dence intervals for probabilities. Technometrics, 50 , 64-68.Hougaard, P. (2012). Analysis of Multivariate Survival Data. Springer Science & Business Media.Ibrahim, J. G., Chen, M.-H., y Sinha, D. (2014). Bayesian Survival Analysis. John Wiley & Sons: Statistics Reference Online.Joe, H. (1997). Multivariate Models and Multivariate Dependence Concepts. CRC Press.Kotz, S., Balakrishnan, N., y Johnson, N. L. (2004). Continuous Multivariate Distributions, Volume 1: Models and Applications. John Wiley & Sons.Kundu, D. (2007). On hybrid censored Weibull distribution. Journal of Statistical Planning and Inference, 137 , 2127-2142.Kundu, D., y Basu, S. (2000). Analysis of incomplete data in presence of competing risks. Journal of Statistical Planning and Inference, 87 , 221-239.Kundu, D., y Dey, A. K. (2009). Estimating the parameters of the Marshall{Olkin bivariate Weibull distribution by EM algorithm. Computational Statistics & Data Analysis, 53 , 956-965.Lai, C.-D., Dong Lin, G., Govindaraju, K., y Pirikahu, S. (2017). A simulation study on the correlation structure of Marshall-Olkin bivariate Weibull distribution. Journal of Statistical Computation and Simulation, 87 , 156-170.Lawless, J. F. (2003). 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Biometrika, 82 , 127-138.Colciencias, Convocatoria 727, doctorados nacionales.InvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/80511/1/license.txtcccfe52f796b7c63423298c2d3365fc6MD51ORIGINAL10767993_Tesis_Doctorado2021.pdf10767993_Tesis_Doctorado2021.pdfTesis de Doctorado en Ciencias Estadísticaapplication/pdf4586238https://repositorio.unal.edu.co/bitstream/unal/80511/2/10767993_Tesis_Doctorado2021.pdf6ef81c2d6a9eda7cc066cefe864a9f72MD52THUMBNAIL10767993_Tesis_Doctorado2021.pdf.jpg10767993_Tesis_Doctorado2021.pdf.jpgGenerated Thumbnailimage/jpeg4557https://repositorio.unal.edu.co/bitstream/unal/80511/3/10767993_Tesis_Doctorado2021.pdf.jpg33f9105d0759aaa50931cb2d79028ec7MD53unal/80511oai:repositorio.unal.edu.co:unal/805112023-07-30 23:03:27.788Repositorio Institucional Universidad Nacional de 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GVyZWNob3MgZGUgYXV0b3IgcXVlIGNvbmxsZXZlIGxhIGRpc3RyaWJ1Y2nDs24gZGUgZXN0b3MgYXJjaGl2b3MgeSBtZXRhZGF0b3MuCkFsIGhhY2VyIGNsaWMgZW4gZWwgc2lndWllbnRlIGJvdMOzbiwgdXN0ZWQgaW5kaWNhIHF1ZSBlc3TDoSBkZSBhY3VlcmRvIGNvbiBlc3RvcyB0w6lybWlub3MuCgpVTklWRVJTSURBRCBOQUNJT05BTCBERSBDT0xPTUJJQSAtIMOabHRpbWEgbW9kaWZpY2FjacOzbiAyNy8yMC8yMDIwCg==

Intervalos de confianza para la confiabilidad de sistemas coherentes no-reparables con estructura dependiente en la familia Weibull.

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