Análisis de Superficies de Respuesta Tipo Beta con Dispersión Variable

Autores:: Melo Martínez, Oscar Orlando
Franco-Soto, Diana Carolina
Melo Martínez, Carlos Eduardo

Tipo de recurso:: Article of journal

Fecha de publicación:: 2026

Institución:: Universidad Pedagógica y Tecnológica de Colombia

Repositorio:: RiUPTC: Repositorio Institucional UPTC

Idioma:: spa

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network_name_str	RiUPTC: Repositorio Institucional UPTC
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dc.title.es-ES.fl_str_mv	Análisis de Superficies de Respuesta Tipo Beta con Dispersión Variable
title	Análisis de Superficies de Respuesta Tipo Beta con Dispersión Variable
spellingShingle	Análisis de Superficies de Respuesta Tipo Beta con Dispersión Variable Dispersión Constante y Variable Metodología de Superficies de Respuesta Modelo Beta Optimización Constant and Variable Dispersion Response Surface Methodology Beta Model Optimization
title_short	Análisis de Superficies de Respuesta Tipo Beta con Dispersión Variable
title_full	Análisis de Superficies de Respuesta Tipo Beta con Dispersión Variable
title_fullStr	Análisis de Superficies de Respuesta Tipo Beta con Dispersión Variable
title_full_unstemmed	Análisis de Superficies de Respuesta Tipo Beta con Dispersión Variable
title_sort	Análisis de Superficies de Respuesta Tipo Beta con Dispersión Variable
dc.creator.fl_str_mv	Melo Martínez, Oscar Orlando Franco-Soto, Diana Carolina Melo Martínez, Carlos Eduardo
dc.contributor.author.spa.fl_str_mv	Melo Martínez, Oscar Orlando Franco-Soto, Diana Carolina Melo Martínez, Carlos Eduardo
dc.subject.none.fl_str_mv	Dispersión Constante y Variable Metodología de Superficies de Respuesta Modelo Beta Optimización Constant and Variable Dispersion Response Surface Methodology Beta Model Optimization
topic	Dispersión Constante y Variable Metodología de Superficies de Respuesta Modelo Beta Optimización Constant and Variable Dispersion Response Surface Methodology Beta Model Optimization
publishDate	2026
dc.date.accessioned.none.fl_str_mv	2026-10-17 23:11:30
dc.date.available.none.fl_str_mv	2026-10-17 23:11:30
dc.date.issued.none.fl_str_mv	2026-10-17
dc.type.spa.fl_str_mv	Artículo de revista
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dc.identifier.issn.none.fl_str_mv	0121-7488
dc.identifier.doi.none.fl_str_mv	10.19053/uptc.01217488.v17.n1.2026.18872
dc.identifier.eissn.none.fl_str_mv	2462-7658
dc.identifier.url.none.fl_str_mv	https://doi.org/10.19053/uptc.01217488.v17.n1.2026.18872
identifier_str_mv	0121-7488 10.19053/uptc.01217488.v17.n1.2026.18872 2462-7658
url	https://doi.org/10.19053/uptc.01217488.v17.n1.2026.18872
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Ferrari, S. y Cribari-Neto, F., “Beta Regression for Modelling Rates and Proportions”, Journal of Applied Statistics, vol. 31, pp. 799-815, 2004. [2] Vasconcellos, K. L. P. y Cribari-Neto, F., “Improved Maximum Likelihood Estimation in a New Class of Beta Regression Models”, Brazilian Journal of Probability and Statistics, vol. 19, pp. 13-31, 2005. [3] Bury, K., “Statistical Distributions in Engineering”. New York, Cambridge University Press, 1999. [4] Johnson, N. L. , Kotz, S. y Balakrishnan, N., “Continuous Univariate Distributions”. New York, JohnWiley & Sons, 1995. [5] Krysicki, W., “On Some New Properties of the Beta Distribution”, Statistics and Probability Letters, vol. 42, pp. 131-137, 1999. [6] Ospina, R., Cribari-Neto, F. y Vasconcellos, K. L. P., “Improved Point and Interval Estimation for a Beta Regression Model”, Computational Statistics & Data Analysis, vol. 51, pp. 960-981, 2006. [7] Simas, A.B., Barreto-Souza, W. y Rocha, A.V., “Improved Estimators for a General Class of Beta Regression Models”, Computational Statistics & Data Analysis, vol. 54, pp. 348-366, 2010. [8] Box, G. E. P. y Wilson, K. B., “On the Experimental Attainment of the Optimum Conditions ”, Journal Royal of the Statistical Society, vol. 13, pp. 1-45, 1951. [9] Hill, W. J. y Hunter, W. G., “A Review of Response Surface Methodology: A Literature Review”, Technometrics, vol. 8, pp. 571-590, 1966. [10] Thompson,W. O., “Secondary Criteria in the Selection of Minimum Bias Design in Two Variables”, Technometrics, vol. 2, pp. 319-328, 1973. [11] Mead, R. y Pike, D. J., “A Review of Responses Surface Methodology from a Biometric Viewpoint”, Biometrics, vol. 31, pp. 830-851, 1975. [12] Lucas, J. M., “Which Response Surfaces Is Best?”, Technometrics, vol. 18, pp. 411-417, 1976. [13] Box, G. E. P. y Draper, N. R., “Measures of Lack of Fit for Response Surface Designs and Predictor Variable Transformations ”, Tecnometrics, vol. 24, pp. 1-8, 1982. [14] Box, G. E. P. y Jones, S., “Split Plot Designs for Robust Product Experimentation ”, Journal Applied Statistics, vol. 19, pp. 3-26, 1992. [15] Draper, N. y Ying, L. H., “A Note on Slope Rotatability Over all Directions”, Journal of Statistical Planning and Inference, vol. 41, pp. 113-119, 1994. [16] Myers, R. H., Montgomery, D. C. y Vinning, G. G., “Generalized Linear Models With Applications in Engineering and the Sciences”. New York, John Wiley & Sons, 2002. Box, G. E. P. y Draper, N. R., “Empirical Model-Building and Response Surfaces”. New York, John Wiley & Sons, 1987. [18] Myers, R. y Montgomery, D., “Response Surface Methodology, Process and Product Optimization Using Designed Experiments”. New York, John Wiley & Sons, 1995. [19] Khuri, A. I. y Cornell, J. A., “Response Surfaces”. New York, Dekker, 1996. [20] McCullagh, P. y Nelder, J., “Generalized Linear Models”. London, Chapman Hall, 1989. [21] McCulloch, C. E. y Searle, S. R., “Generalized, Linear and Mixed Models”. New York, John Wiley & Sons, 2001. [22] Lee, Y. J. y Nelder, J. A., “Analysis of Ulcer Data Using Hierarchical Generalized Linear Models”, Journal of Quality Technology, vol. 21, pp. 191-202, 2002. [23] Dobson, A. J., “An Introduction to Generalized Linear Models”. Boca Raton, 2nd ed. Chapman Hall, 2002. [24] Smith, D. M. y Ridout, M. S., “Optimal Designs for Criteria Involving log(potency) in Comparative Binary Bioassays”, Journal of Statistical Planning and Inference, vol. 113, pp. 617-632, 2003. [25] Khuri, A. I., Mukherjee, B., Sinha, B. K. y Ghosh, M., “Design Issues for Generalized Linear Models: A Review”, Statistical Science, vol. 21, pp. 376-399, 2006. [26] Carbajal-Morán, H., “Fotocatálisis solar en la recuperación de aguas grises para la agricultura: innovación y eficiencia por control de procesos con PLC S7-1500 “. Universidad Nacional de Huancavelica, 2023. [27] Cribari-Neto, F. y Vasconcellos, K., “Nearly Unbiased Maximun Likelihood Estimation for the Beta Distribution”, Journal of Statistical Computation and Simulation, vol. 72, pp. 107-118, 2002. [28] Atkinson, A., “Plots, Transformations and Regression: An Introduction to Graphical Methods of Diagnostic Regression Analysis”. New York, Oxford University Press, 1985. [29] Espinheira, P. L. , Ferrari, S. L. P. y Cribari-Neto, F., “On Beta Regression Residuals”, Journal of Applied Statistics, vol. 35, pp. 407-419, 2008. [30] R Development Core Team, “R: A Language and Environment for Statistical Computing”. Vienna, R Foundation for Statistical Computing URL http://www.Rproject. org/, 2026. [31] Smithson, M. y Verkuilen, J., “A Better Lemon Squeezer? Maximum-Likelihood Regression with Beta-Distributed Depedent Variables”, Psychological Methods, vol. 11, pp. 54-71, 2006.
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dc.relation.citationvolume.spa.fl_str_mv	17
dc.relation.ispartofjournal.spa.fl_str_mv	Ciencia en Desarrollo
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dc.relation.citationedition.spa.fl_str_mv	Núm. 1 , Año 2026 : Vol. 17 Núm. 1 (2026): Vol. 17 Núm. 1 (2026): Vol. 17 Núm. 1 (2026): Vol 17, Núm.1 (2026): Enero - Junio
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spelling	Melo Martínez, Oscar OrlandoFranco-Soto, Diana CarolinaMelo Martínez, Carlos Eduardo2026-10-17 23:11:302026-10-17 23:11:302026-10-170121-748810.19053/uptc.01217488.v17.n1.2026.188722462-7658https://doi.org/10.19053/uptc.01217488.v17.n1.2026.18872application/pdfspaUniversidad 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/18872Dispersión Constante y VariableMetodología de Superficies de RespuestaModelo BetaOptimizaciónConstant and Variable DispersionResponse Surface MethodologyBeta ModelOptimizationAnálisis de Superficies de Respuesta Tipo Beta con Dispersión VariableArtículo 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_970fb48d4fbd8a85Ferrari, S. y Cribari-Neto, F., “Beta Regression for Modelling Rates and Proportions”, Journal of Applied Statistics, vol. 31, pp. 799-815, 2004. [2] Vasconcellos, K. L. P. y Cribari-Neto, F., “Improved Maximum Likelihood Estimation in a New Class of Beta Regression Models”, Brazilian Journal of Probability and Statistics, vol. 19, pp. 13-31, 2005. [3] Bury, K., “Statistical Distributions in Engineering”. New York, Cambridge University Press, 1999. [4] Johnson, N. L. , Kotz, S. y Balakrishnan, N., “Continuous Univariate Distributions”. New York, JohnWiley & Sons, 1995. [5] Krysicki, W., “On Some New Properties of the Beta Distribution”, Statistics and Probability Letters, vol. 42, pp. 131-137, 1999. [6] Ospina, R., Cribari-Neto, F. y Vasconcellos, K. L. P., “Improved Point and Interval Estimation for a Beta Regression Model”, Computational Statistics & Data Analysis, vol. 51, pp. 960-981, 2006. [7] Simas, A.B., Barreto-Souza, W. y Rocha, A.V., “Improved Estimators for a General Class of Beta Regression Models”, Computational Statistics & Data Analysis, vol. 54, pp. 348-366, 2010. [8] Box, G. E. P. y Wilson, K. B., “On the Experimental Attainment of the Optimum Conditions ”, Journal Royal of the Statistical Society, vol. 13, pp. 1-45, 1951. [9] Hill, W. J. y Hunter, W. G., “A Review of Response Surface Methodology: A Literature Review”, Technometrics, vol. 8, pp. 571-590, 1966. [10] Thompson,W. O., “Secondary Criteria in the Selection of Minimum Bias Design in Two Variables”, Technometrics, vol. 2, pp. 319-328, 1973. [11] Mead, R. y Pike, D. J., “A Review of Responses Surface Methodology from a Biometric Viewpoint”, Biometrics, vol. 31, pp. 830-851, 1975. [12] Lucas, J. M., “Which Response Surfaces Is Best?”, Technometrics, vol. 18, pp. 411-417, 1976. [13] Box, G. E. P. y Draper, N. R., “Measures of Lack of Fit for Response Surface Designs and Predictor Variable Transformations ”, Tecnometrics, vol. 24, pp. 1-8, 1982. [14] Box, G. E. P. y Jones, S., “Split Plot Designs for Robust Product Experimentation ”, Journal Applied Statistics, vol. 19, pp. 3-26, 1992. [15] Draper, N. y Ying, L. H., “A Note on Slope Rotatability Over all Directions”, Journal of Statistical Planning and Inference, vol. 41, pp. 113-119, 1994. [16] Myers, R. H., Montgomery, D. C. y Vinning, G. G., “Generalized Linear Models With Applications in Engineering and the Sciences”. New York, John Wiley & Sons, 2002.Box, G. E. P. y Draper, N. R., “Empirical Model-Building and Response Surfaces”. New York, John Wiley & Sons, 1987. [18] Myers, R. y Montgomery, D., “Response Surface Methodology, Process and Product Optimization Using Designed Experiments”. New York, John Wiley & Sons, 1995. [19] Khuri, A. I. y Cornell, J. A., “Response Surfaces”. New York, Dekker, 1996. [20] McCullagh, P. y Nelder, J., “Generalized Linear Models”. London, Chapman Hall, 1989. [21] McCulloch, C. E. y Searle, S. R., “Generalized, Linear and Mixed Models”. New York, John Wiley & Sons, 2001. [22] Lee, Y. J. y Nelder, J. A., “Analysis of Ulcer Data Using Hierarchical Generalized Linear Models”, Journal of Quality Technology, vol. 21, pp. 191-202, 2002. [23] Dobson, A. J., “An Introduction to Generalized Linear Models”. Boca Raton, 2nd ed. Chapman Hall, 2002. [24] Smith, D. M. y Ridout, M. S., “Optimal Designs for Criteria Involving log(potency) in Comparative Binary Bioassays”, Journal of Statistical Planning and Inference, vol. 113, pp. 617-632, 2003. [25] Khuri, A. I., Mukherjee, B., Sinha, B. K. y Ghosh, M., “Design Issues for Generalized Linear Models: A Review”, Statistical Science, vol. 21, pp. 376-399, 2006. [26] Carbajal-Morán, H., “Fotocatálisis solar en la recuperación de aguas grises para la agricultura: innovación y eficiencia por control de procesos con PLC S7-1500 “. Universidad Nacional de Huancavelica, 2023. [27] Cribari-Neto, F. y Vasconcellos, K., “Nearly Unbiased Maximun Likelihood Estimation for the Beta Distribution”, Journal of Statistical Computation and Simulation, vol. 72, pp. 107-118, 2002. [28] Atkinson, A., “Plots, Transformations and Regression: An Introduction to Graphical Methods of Diagnostic Regression Analysis”. New York, Oxford University Press, 1985. [29] Espinheira, P. L. , Ferrari, S. L. P. y Cribari-Neto, F., “On Beta Regression Residuals”, Journal of Applied Statistics, vol. 35, pp. 407-419, 2008. [30] R Development Core Team, “R: A Language and Environment for Statistical Computing”. Vienna, R Foundation for Statistical Computing URL http://www.Rproject. org/, 2026. [31] Smithson, M. y Verkuilen, J., “A Better Lemon Squeezer? Maximum-Likelihood Regression with Beta-Distributed Depedent Variables”, Psychological Methods, vol. 11, pp. 54-71, 2006.128111517Ciencia en Desarrollohttps://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/download/18872/17386Núm. 1 , Año 2026 : Vol. 17 Núm. 1 (2026): Vol. 17 Núm. 1 (2026): Vol. 17 Núm. 1 (2026): Vol 17, Núm.1 (2026): Enero - JunioPublicationOREORE.xmltext/xml2709https://repositorio.uptc.edu.co/bitstreams/73f9e142-e195-427d-8f37-8d1cefa4cf25/download61852d07b7c7fa3185924722fcd07220MD51falseAnonymousREAD001/21880oai:repositorio.uptc.edu.co:001/218802026-10-30 06:59:08.624https://creativecommons.org/licenses/by-nc-nd/4.0/deed.esmetadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCbdigital@metabiblioteca.com

Análisis de Superficies de Respuesta Tipo Beta con Dispersión Variable

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