Superficie de respuesta con bloques en supervivencia

Ilustraciones y tablas

Autores:: Chávez Rojas, Ana Patricia

Tipo de recurso:

Fecha de publicación:: 2020

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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repository_id_str
dc.title.spa.fl_str_mv	Superficie de respuesta con bloques en supervivencia
dc.title.translated.eng.fl_str_mv	Response surface methodology with blocks in survival
title	Superficie de respuesta con bloques en supervivencia
spellingShingle	Superficie de respuesta con bloques en supervivencia 510 - Matemáticas Modelos estadísticos Statistical models Parameter estimation Estimación de parámetros Probabilities Probabilidades Diseño de experimentos Modelo de Cox Análisis de supervivencia Metodología de superficies de respuestas Design of experiments Cox model Response surfaces methodology Survival analysis
title_short	Superficie de respuesta con bloques en supervivencia
title_full	Superficie de respuesta con bloques en supervivencia
title_fullStr	Superficie de respuesta con bloques en supervivencia
title_full_unstemmed	Superficie de respuesta con bloques en supervivencia
title_sort	Superficie de respuesta con bloques en supervivencia
dc.creator.fl_str_mv	Chávez Rojas, Ana Patricia
dc.contributor.advisor.none.fl_str_mv	Melo Martínez, Oscar Orlando
dc.contributor.author.none.fl_str_mv	Chávez Rojas, Ana Patricia
dc.subject.ddc.spa.fl_str_mv	510 - Matemáticas
topic	510 - Matemáticas Modelos estadísticos Statistical models Parameter estimation Estimación de parámetros Probabilities Probabilidades Diseño de experimentos Modelo de Cox Análisis de supervivencia Metodología de superficies de respuestas Design of experiments Cox model Response surfaces methodology Survival analysis
dc.subject.other.none.fl_str_mv	Modelos estadísticos Statistical models
dc.subject.lemb.none.fl_str_mv	Parameter estimation Estimación de parámetros Probabilities Probabilidades
dc.subject.proposal.spa.fl_str_mv	Diseño de experimentos Modelo de Cox Análisis de supervivencia Metodología de superficies de respuestas
dc.subject.proposal.eng.fl_str_mv	Design of experiments Cox model Response surfaces methodology Survival analysis
description	Ilustraciones y tablas
publishDate	2020
dc.date.issued.none.fl_str_mv	2020-06
dc.date.accessioned.none.fl_str_mv	2021-09-13T18:03:47Z
dc.date.available.none.fl_str_mv	2021-09-13T18:03:47Z
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/80167
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/80167 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.references.spa.fl_str_mv	Aalen, O. O. (1989), ‘A linear regression model for the analysis of life times’, Statistics in medicine 8(8), 907–925. Aalen, O. O. (1993), ‘Further results on the non-parametric linear regression model in survival analysis’, Statistics in medicine 12(17), 1569–1588. Andersen, P. K. & Gill, R. D. (1982), ‘Cox’s regression model for counting processes: a large sample study’. Ata, N. & Tekin, M. (2007), ‘Cox regression models with nonproportional hazards applied to lung cancer survival data’, Hacettepe Journal of Mathematics and Statistics 36(2), 157–167. Bender, R., Augustin, T. & Blettner, M. (2005), ‘Generating survival times to simulate Cox proportional hazards models’, Statistics in medicine 24(11), 1713–1723. Bonferroni, C. (1936), ‘Teoria statistica delle classi e calcolo delle probabilita’, Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commericiali di Firenze 8, 3–62. Box, G. E. P. & Draper, N. R. (1982), ‘Measures of lack of fit for response surface designs and predictor variable transformations’, Technometrics 24, 1–8. Box, G. E. P. & Draper, N. R. (2007), Response Surfaces, Mixtures, and Ridge Analyses, Wiley Series in Probability and Statistics, New York. Box, G. E. P. & Wilson, K. B. (1951), ‘On the experimental attainment of optimum conditions’, Journal of the Royal Statistical Society 13, 1–45. Brenneman, W. A., Myers, W. R. & Myers, R. H. (2005), ‘A dual-response approach to robust parameter design for a generalizad linear model’, Journal of Quality Technology 37, 130–138. Breslow, N. E. (1972), ‘Discussion following “Regression models and life table” by D. R. Cox’, Journal of the Royal Statistical Society Series B 34, 187–220. Breslow, N. E. (1974), ‘Covariance analysis of censored survival data’, Biometrics 30(1), 89–99. Buchholz, A. & Sauerbrei, W. (2011), ‘Comparison of procedures to assess non-linear and time- varying effects in multivariable models for survival data’, Biometrical Journal 53(2), 308–331. Chen, M.-H., Ibrahim, J. G. & Sinha, D. (1999), ‘A new bayesian model for survival data with a surviving fraction’, Journal of the American Statistical Association 94(447), 909–919. Chiou, J., Müller, H. &Wang, J. (2004), ‘Functional response models’, Statistica Sinica 14, 675–693. Cochran, W, G. & Cox, G. M. (1992), Experimental Designs, Wiley, New York. Collett, D. (2003), Modelling survival data, in ‘Modelling Survival Data in Medical Research’, Springer. Cox, D. R. (1972), ‘Regression models and life-tables’, Journal of the Royal Statistical Society. Series B 34(2), 187–220. Cox, D. R. (1975), ‘Partial likelihood’, Biometrika 62, 269–276. Cox, D. R. & Snell, E. J. (1968), ‘A general definition of residuals’, Journal of the Royal Statistical Society: Series B (Methodological) 30(2), 248–265. Das, M. K., Sahu, P. K., Rao, G. S., Mukkanti, K. & Silpavathi, L. (2014), ‘Application of response surface method to evaluate the cytotoxic potency of Ulva fasciata Delile, a marine macro alga’, Saudi Journal of Biological Sciences 21(6), 539 – 546. Das, R. N. (2009), ‘Response surface methodology in improving mean lifetime’, ProbStat Forum 2, 08–21. Debusho, L. K. & Chibayambuya, G. (2013), ‘Efficient response-surface designs with unknown block sizes’, South African Statistical Journal 47, 15–22. Derringer, G. C. & Suich, R. (1980), ‘Simultaneous optimization of several responses variables’, Journal of Quality Technology 12, 214–219. Draper, N. & Ying, L. H. (1994), ‘A note on slope rotatability over all directions’, Journal of Statistical Planning and Inference 41, 113–119. Efron, B. (1977), ‘The efficiency of Cox’s likelihood function for censored data’, Journal of the American Statistical Association 72(359), 557–565. Flores, C. J. (2011), Modelo de regresión de Cox con métodos flexibles en pacientes con linfoma no Hodgkin, Tesis de master, Universitat Politecnica de Catalunya, Barcelona. García, J. J. (2012), ‘Análisis de supervivencia aplicado al estudio de la mortalidad en injertos de Inchi (Caryodendron orinocense Karsten)’, Revista Científica UDO Agrícola 12(4), 759–769. García, J., Pérez, H. M. & Perdomo, D. (2009), ‘Evaluación de dos métodos de propagación asexual en Inchi (Caryodendron orinocense karsten)’, Revista Científica UDO Agrícola 9(4), 782–792. Giolo, S. R. & Colosimo, E. A. (2006), Análise de sobrevivencia aplicada, Edgard Blucher. Gómez, G. (2004), Análisis de supervivencia, Universidad Politécnica de Catalunya, Barcelona. Guanghui, W. & Douglas, E. S. (2008), ‘Estimating cumulative treatment effects in the presence of nonproportional hazards’, Biometrics 64(3), 724–732. Guanghui, W. & Douglas, E. S. (2014), ‘A measure for assessing functions of time-varying effects in survival analysis’, Open Journal of Statistics 4(11), 977–998. Harrell, J. & Frank, E. (2015), Cox proportional hazards regression model, in ‘Regression Modeling Strategies’, Springer, pp. 475–519. Harrington, J. E. C. (1965), ‘The desirability function’, Industrial Quality Control 21(10), 494–498. Hill, W. J. & Hunter, W. G. (1966), ‘A review of response surface methodology: A literature review’, Technometrics 8, 571–590. Huffer, F. W. & McKeague, I. W. (1991), ‘Weighted least squares estimation for Aalen’s additive risk model’, Journal of the American Statistical Association 86(413), 114–129. Johansen, S. (1983), ‘An extension of cox’s regression model’, International Statistical Review/Revue Internationale de Statistique pp. 165–174. Kalbfleisch, J. D. & Prentice, R. L. (1973), ‘Marginal likelihoods based on Cox’s regression and life model’, Biometrika 60(2), 267–278. Kalbfleisch, J. D. & Prentice, R. L. (1980), The statistical analysis of failure time data, John Wiley & Sons, New York. Kalbfleisch, J. D. & Prentice, R. L. (2011), The statistical analysis of failure time data, Vol. 360, John Wiley & Sons. Kaplan, E. & Meier, P. (1958), ‘Nonparametric estimation from incomplete observations’, Journal of the American Statistical Association 53, 457–481. Khuri, A. I. & Colon, M. (1981), ‘Simultaneous optimization of multiples responses represented by polynomial regression functions’, Technometrics 23(4), 363–375. Klein, J. P. & Moeschberger, M. L. (1997), Survival analysis: techniques for censored and truncated data, Springer Science, New York. Klein, J. P. & Moeschberger, M. L. (2003), Survival analysis Techniques for censored and truncated data, Springer, New York. Klein, J. P. & Moeschberger, M. L. (2006), Survival analysis: techniques for censored and truncated data, Springer Science & Business Media. Kudelka, W. (2007), ‘Use of response surface methodology to evaluate the survival rate of yogurt bacteria in natural bio-yoghurts of cow and goat milk’, Milchwissenschaft 63(3), 08–21. Lin, D. & Ying, Z. (1995), ‘Semiparametric analysis of general additivemultiplicative hazard models for counting processes’, Annals of Statistics 23, 1712–1734. Lucas, J. M. (1976), ‘Which response surfaces is best?’, Technometrics 18, 411-417. Mead, R. & Pike, D. J. (1975), ‘A review of responses surface methodology from a biometric viewpoint’, Biometrics 31, 830–851. Montgomery, D. (2005), Diseño y análisis de experimentos, Limusa Wiley, México. Montgomery, D. C. (2012), Design and Analyisis of Experiments, 5 edn, John Wiley & Sons. Myers, R. H. & Montgomery, D. C. (1995), Response Surface Methodology, John Wiley & Sons. Myers, R. H., Montgomery, D. C. & Anderson-Cook, C. M. (2016), Response surface methodology: process and product optimization using designed experiments, John Wiley & Sons. Myers, R. H., Montgomery, D. C. & Vinning, G. G. (2002), Generalized Linear Models. With Applications in Engineering and the Sciences, John Wiley & Sons. Oliveira, T., Leal, C. & Oliveira, A. (2014), Stochastic response surface methodology in medicine with censored/uncensored data analysis, in Biomed, ed., ‘Applied Numerical Mathematics and Scientific Computation’, Athens, Greece, pp. 92–98. Panagiotis, N. S. & George-John, E. N. (2000), ‘Development and evaluation of a model predicting the survival of Escherichia coli o157:h7 NCTC 12900 in Homemade Eggplant salad at various temperatures, pHs, and oregano essential oil concentrations’, Applied and Environmental Microbiology 66(4), 1646–1653. Peace, K. E. (2009), Design and Analysis of Clinical Trials with Time-to-Event Endpoints, CRC Press. R Development Core Team (2020), R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, URL http://www.R-project.org/. Sauerbrei, W., Royston, P. & Look, M. (2007), ‘A new proposal for multivariable modelling of time-varying effects in survival data based on fractional polynomial time-transformation’, Biometrical Journal 49(3), 453–73. Scheike, T. & Martinussen, T. (2002), ‘A flexible additive multiplicative hazard model’, Biometrika 89, 283–298. Scheike, T. H. & Zhang, M.-J. (2002), ‘An additive-multiplicative Cox-Aalen regression model’, Scandinavian Journal of Statistics 29(1), 75–88. Scheike, T. H. & Zhang, M.-J. (2005), Predicting cumulative incidence probability: Marginal and cause-specific modelling, Department of Biostatistics, University of Copenhagen. Schumacher, M. (1984), ‘Two-sample tests of cram´er-von mises-and kolmogorovsmirnov-type for randomly censored data’, International Statistical Review/Revue Internationale de Statistique pp. 263–281. Song, X. & Wang, C. Y. (2008), ‘Semiparametric approaches for joint modeling of longitudinal and survival data with time-varying coefficients’, Biometrics 64(2), 557–566. Stephen, L. L., Randall, P. N. & Evens, T. J. (2010), ‘An artificial diet for diaprepes abbreviatus(coleoptera: Curculionidae) optimized for larval survival’, Florida Entomologist 93(1), 56–62. Tsiatis, A. A. (1981), ‘A large sample study of Cox’s regression model’, The Annals of Statistics pp. 93–108. Vining, G. (1998), ‘A compromise approach to multiresponse optimization’, Journal of Quality Technology 30(4), 309–313. Zeng, D. & Lin, D. Y. (2007), ‘Maximum likelihood estimation in semiparametric regression models with censored data’, Journal of the Royal Statistical Society: Series B (Statistical Methodology) 69(4), 507–564. Zeng, D., Cai, J. & Shen, Y. (2006), ‘Semiparametric additive risks model for interval-censored data’, Statistica Sinica 16(1), 287–302. Zeng, D., Yin, G. & Ibrahim, J. G. (2006), ‘Semiparametric transformation models for survival data with a cure fraction’, Journal of the American Statistical Association 101(474), 670–684. Zeng, D. et al. (2004), ‘Estimating marginal survival function by adjusting for dependent censoring using many covariates’, The Annals of Statistics 32(4), 1533–1555.
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ciencias - Maestría en Ciencias - Estadística
dc.publisher.department.spa.fl_str_mv	Departamento de Estadística
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de 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_abf2Melo Martínez, Oscar Orlando653518c1f1441d004f4edeffc2a59886Chávez Rojas, Ana Patriciaf481b6cfd4a578ee3df50e1385aa192d2021-09-13T18:03:47Z2021-09-13T18:03:47Z2020-06https://repositorio.unal.edu.co/handle/unal/80167Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Ilustraciones y tablasIn this work, a methodology is proposed to fit a survival model to a response surface in the presence of blocks, in order that this methodology allows improving the estimation of parameters and prediction in models where the variable of interest is observed in of time. Also, was developed an adaptation of the classical correction methods for ties data to the proposed methodology. Theoretical development was carried out for the construction, estimation, and validation of assumptions, which was developed using the Cox proportional hazard model and the response surfaces methodology. To evaluate the performance of the methodology in comparison with other methodologies, real and simulated data were used. The results also show the fact that by combining the proportional hazards model with the response surfaces methodology, it is possible to identify the levels of the treatments that optimize the response variable. Finally, it is concluded that this methodology has the advantage of being able to include a local control (block) that allows reducing experimental error, improving efficiency by detecting minor differences between treatments, which allows making comparisons over the treatments more reliable.En este trabajo se propone una metodología para ajustar un modelo de supervivencia a una superficie de respuesta en presencia de bloques, con la finalidad de que dicha metodología permita mejorar la estimación de parámetros y predicción en modelos donde la variable de interés es observada en el tiempo hasta la ocurrencia de un evento. Se llevó a cabo el desarrollo teórico para la construcción, estimación y validación de supuestos, utilizando como base el modelo de riesgos proporcionales de Cox y la metodología de superficies de respuesta. Asimismo, se desarrolló una adaptación de los métodos clásicos de corrección de empates para la metodología propuesta. Para evaluar el desempeño de la metodología en comparación con otras metodologías se utilizaron datos reales y simulados. Los resultados ponen en evidencia el hecho de que al combinar el modelo de riesgos proporcionales con la metodología de superficies de respuesta se puede identificar los niveles de los tratamientos que optimizan la variable respuesta. Finalmente, se concluye que esta metodología tiene la ventaja de poder incluir un control local (bloque) que permite reducir el error experimental, mejorar la eficiencia al detectar menores diferencias entre los tratamientos, con lo cual se pueden hacer comparaciones de los tratamientos más confiables. (Texto tomado de la fuente).MaestríaMagíster en Ciencias - Estadística113 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaDepartamento de EstadísticaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá510 - MatemáticasModelos estadísticosStatistical modelsParameter estimationEstimación de parámetrosProbabilitiesProbabilidadesDiseño de experimentosModelo de CoxAnálisis de supervivenciaMetodología de superficies de respuestasDesign of experimentsCox modelResponse surfaces methodologySurvival analysisSuperficie de respuesta con bloques en supervivenciaResponse surface methodology with blocks in survivalTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAalen, O. O. (1989), ‘A linear regression model for the analysis of life times’, Statistics in medicine 8(8), 907–925.Aalen, O. O. (1993), ‘Further results on the non-parametric linear regression model in survival analysis’, Statistics in medicine 12(17), 1569–1588.Andersen, P. K. & Gill, R. D. (1982), ‘Cox’s regression model for counting processes: a large sample study’.Ata, N. & Tekin, M. (2007), ‘Cox regression models with nonproportional hazards applied to lung cancer survival data’, Hacettepe Journal of Mathematics and Statistics 36(2), 157–167.Bender, R., Augustin, T. & Blettner, M. (2005), ‘Generating survival times to simulate Cox proportional hazards models’, Statistics in medicine 24(11), 1713–1723.Bonferroni, C. (1936), ‘Teoria statistica delle classi e calcolo delle probabilita’, Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commericiali di Firenze 8, 3–62.Box, G. E. P. & Draper, N. R. (1982), ‘Measures of lack of fit for response surface designs and predictor variable transformations’, Technometrics 24, 1–8.Box, G. E. P. & Draper, N. R. (2007), Response Surfaces, Mixtures, and Ridge Analyses, Wiley Series in Probability and Statistics, New York.Box, G. E. P. & Wilson, K. B. (1951), ‘On the experimental attainment of optimum conditions’, Journal of the Royal Statistical Society 13, 1–45.Brenneman, W. A., Myers, W. R. & Myers, R. H. (2005), ‘A dual-response approach to robust parameter design for a generalizad linear model’, Journal of Quality Technology 37, 130–138.Breslow, N. E. (1972), ‘Discussion following “Regression models and life table” by D. R. Cox’, Journal of the Royal Statistical Society Series B 34, 187–220.Breslow, N. E. (1974), ‘Covariance analysis of censored survival data’, Biometrics 30(1), 89–99.Buchholz, A. & Sauerbrei, W. (2011), ‘Comparison of procedures to assess non-linear and time- varying effects in multivariable models for survival data’, Biometrical Journal 53(2), 308–331.Chen, M.-H., Ibrahim, J. G. & Sinha, D. (1999), ‘A new bayesian model for survival data with a surviving fraction’, Journal of the American Statistical Association 94(447), 909–919.Chiou, J., Müller, H. &Wang, J. (2004), ‘Functional response models’, Statistica Sinica 14, 675–693.Cochran, W, G. & Cox, G. M. (1992), Experimental Designs, Wiley, New York.Collett, D. (2003), Modelling survival data, in ‘Modelling Survival Data in Medical Research’, Springer.Cox, D. R. (1972), ‘Regression models and life-tables’, Journal of the Royal Statistical Society. Series B 34(2), 187–220.Cox, D. R. (1975), ‘Partial likelihood’, Biometrika 62, 269–276.Cox, D. R. & Snell, E. J. (1968), ‘A general definition of residuals’, Journal of the Royal Statistical Society: Series B (Methodological) 30(2), 248–265.Das, M. K., Sahu, P. K., Rao, G. S., Mukkanti, K. & Silpavathi, L. (2014), ‘Application of response surface method to evaluate the cytotoxic potency of Ulva fasciata Delile, a marine macro alga’, Saudi Journal of Biological Sciences 21(6), 539 – 546.Das, R. N. (2009), ‘Response surface methodology in improving mean lifetime’, ProbStat Forum 2, 08–21.Debusho, L. K. & Chibayambuya, G. (2013), ‘Efficient response-surface designs with unknown block sizes’, South African Statistical Journal 47, 15–22.Derringer, G. C. & Suich, R. (1980), ‘Simultaneous optimization of several responses variables’, Journal of Quality Technology 12, 214–219.Draper, N. & Ying, L. H. (1994), ‘A note on slope rotatability over all directions’, Journal of Statistical Planning and Inference 41, 113–119.Efron, B. (1977), ‘The efficiency of Cox’s likelihood function for censored data’, Journal of the American Statistical Association 72(359), 557–565.Flores, C. J. (2011), Modelo de regresión de Cox con métodos flexibles en pacientes con linfoma no Hodgkin, Tesis de master, Universitat Politecnica de Catalunya, Barcelona.García, J. J. (2012), ‘Análisis de supervivencia aplicado al estudio de la mortalidad en injertos de Inchi (Caryodendron orinocense Karsten)’, Revista Científica UDO Agrícola 12(4), 759–769.García, J., Pérez, H. M. & Perdomo, D. (2009), ‘Evaluación de dos métodos de propagación asexual en Inchi (Caryodendron orinocense karsten)’, Revista Científica UDO Agrícola 9(4), 782–792.Giolo, S. R. & Colosimo, E. A. (2006), Análise de sobrevivencia aplicada, Edgard Blucher.Gómez, G. (2004), Análisis de supervivencia, Universidad Politécnica de Catalunya, Barcelona.Guanghui, W. & Douglas, E. S. (2008), ‘Estimating cumulative treatment effects in the presence of nonproportional hazards’, Biometrics 64(3), 724–732.Guanghui, W. & Douglas, E. S. (2014), ‘A measure for assessing functions of time-varying effects in survival analysis’, Open Journal of Statistics 4(11), 977–998.Harrell, J. & Frank, E. (2015), Cox proportional hazards regression model, in ‘Regression Modeling Strategies’, Springer, pp. 475–519.Harrington, J. E. C. (1965), ‘The desirability function’, Industrial Quality Control 21(10), 494–498.Hill, W. J. & Hunter, W. G. (1966), ‘A review of response surface methodology: A literature review’, Technometrics 8, 571–590.Huffer, F. W. & McKeague, I. W. (1991), ‘Weighted least squares estimation for Aalen’s additive risk model’, Journal of the American Statistical Association 86(413), 114–129.Johansen, S. (1983), ‘An extension of cox’s regression model’, International Statistical Review/Revue Internationale de Statistique pp. 165–174.Kalbfleisch, J. D. & Prentice, R. L. (1973), ‘Marginal likelihoods based on Cox’s regression and life model’, Biometrika 60(2), 267–278.Kalbfleisch, J. D. & Prentice, R. L. (1980), The statistical analysis of failure time data, John Wiley & Sons, New York.Kalbfleisch, J. D. & Prentice, R. L. (2011), The statistical analysis of failure time data, Vol. 360, John Wiley & Sons.Kaplan, E. & Meier, P. (1958), ‘Nonparametric estimation from incomplete observations’, Journal of the American Statistical Association 53, 457–481.Khuri, A. I. & Colon, M. (1981), ‘Simultaneous optimization of multiples responses represented by polynomial regression functions’, Technometrics 23(4), 363–375.Klein, J. P. & Moeschberger, M. L. (1997), Survival analysis: techniques for censored and truncated data, Springer Science, New York.Klein, J. P. & Moeschberger, M. L. (2003), Survival analysis Techniques for censored and truncated data, Springer, New York.Klein, J. P. & Moeschberger, M. L. (2006), Survival analysis: techniques for censored and truncated data, Springer Science & Business Media.Kudelka, W. (2007), ‘Use of response surface methodology to evaluate the survival rate of yogurt bacteria in natural bio-yoghurts of cow and goat milk’, Milchwissenschaft 63(3), 08–21.Lin, D. & Ying, Z. (1995), ‘Semiparametric analysis of general additivemultiplicative hazard models for counting processes’, Annals of Statistics 23, 1712–1734.Lucas, J. M. (1976), ‘Which response surfaces is best?’, Technometrics 18, 411-417.Mead, R. & Pike, D. J. (1975), ‘A review of responses surface methodology from a biometric viewpoint’, Biometrics 31, 830–851.Montgomery, D. (2005), Diseño y análisis de experimentos, Limusa Wiley, México.Montgomery, D. C. (2012), Design and Analyisis of Experiments, 5 edn, John Wiley & Sons.Myers, R. H. & Montgomery, D. C. (1995), Response Surface Methodology, John Wiley & Sons.Myers, R. H., Montgomery, D. C. & Anderson-Cook, C. M. (2016), Response surface methodology: process and product optimization using designed experiments, John Wiley & Sons.Myers, R. H., Montgomery, D. 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(2004), ‘Estimating marginal survival function by adjusting for dependent censoring using many covariates’, The Annals of Statistics 32(4), 1533–1555.Público generalLICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/80167/1/license.txtcccfe52f796b7c63423298c2d3365fc6MD51ORIGINAL39629109.2021.pdf39629109.2021.pdfTesis de Maestría en Ciencias - Estadísticaapplication/pdf16793689https://repositorio.unal.edu.co/bitstream/unal/80167/2/39629109.2021.pdf2408e45f23e745cf81a48289bbaa2936MD52THUMBNAIL39629109.2021.pdf.jpg39629109.2021.pdf.jpgGenerated Thumbnailimage/jpeg4029https://repositorio.unal.edu.co/bitstream/unal/80167/3/39629109.2021.pdf.jpg8e2933795d7bf0a0ea54385381782f51MD53unal/80167oai:repositorio.unal.edu.co:unal/801672023-07-27 23:04:07.333Repositorio Institucional Universidad Nacional de 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