Monitoreo de perfiles no lineales multivariados usando un enfoque de datos funcionales
ilustraciones, gráficas, tablas
- Autores:
-
Espinosa Moreno, Juan Carlos
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/80801
- Palabra clave:
- 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
Análisis de datos funcionales
Método de Montecarlo
Fuynctional data analysis
Monte-Carlo method
Outlyingness
Monitoring
Functional
Profiles
MFPCA
Claeskens
Mahalanobis
Monitoreo
Funcional
Perfiles
Análisis estadístico
Statistical analysis
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Monitoreo de perfiles no lineales multivariados usando un enfoque de datos funcionales |
| dc.title.translated.eng.fl_str_mv |
Multivariate nonlinear profiles monitoring using a functional data approach |
| title |
Monitoreo de perfiles no lineales multivariados usando un enfoque de datos funcionales |
| spellingShingle |
Monitoreo de perfiles no lineales multivariados usando un enfoque de datos funcionales 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Análisis de datos funcionales Método de Montecarlo Fuynctional data analysis Monte-Carlo method Outlyingness Monitoring Functional Profiles MFPCA Claeskens Mahalanobis Monitoreo Funcional Perfiles Análisis estadístico Statistical analysis |
| title_short |
Monitoreo de perfiles no lineales multivariados usando un enfoque de datos funcionales |
| title_full |
Monitoreo de perfiles no lineales multivariados usando un enfoque de datos funcionales |
| title_fullStr |
Monitoreo de perfiles no lineales multivariados usando un enfoque de datos funcionales |
| title_full_unstemmed |
Monitoreo de perfiles no lineales multivariados usando un enfoque de datos funcionales |
| title_sort |
Monitoreo de perfiles no lineales multivariados usando un enfoque de datos funcionales |
| dc.creator.fl_str_mv |
Espinosa Moreno, Juan Carlos |
| dc.contributor.advisor.spa.fl_str_mv |
Guevara González, Rubén Darío |
| dc.contributor.author.spa.fl_str_mv |
Espinosa Moreno, Juan Carlos |
| dc.subject.ddc.spa.fl_str_mv |
510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas |
| topic |
510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Análisis de datos funcionales Método de Montecarlo Fuynctional data analysis Monte-Carlo method Outlyingness Monitoring Functional Profiles MFPCA Claeskens Mahalanobis Monitoreo Funcional Perfiles Análisis estadístico Statistical analysis |
| dc.subject.lemb.spa.fl_str_mv |
Análisis de datos funcionales Método de Montecarlo |
| dc.subject.lemb.eng.fl_str_mv |
Fuynctional data analysis Monte-Carlo method |
| dc.subject.proposal.eng.fl_str_mv |
Outlyingness Monitoring Functional Profiles MFPCA Claeskens |
| dc.subject.proposal.spa.fl_str_mv |
Mahalanobis Monitoreo Funcional Perfiles |
| dc.subject.unesco.spa.fl_str_mv |
Análisis estadístico |
| dc.subject.unesco.eng.fl_str_mv |
Statistical analysis |
| description |
ilustraciones, gráficas, tablas |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2022-01-11T20:24:29Z |
| dc.date.available.none.fl_str_mv |
2022-01-11T20:24:29Z |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
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http://purl.org/redcol/resource_type/TM |
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acceptedVersion |
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https://repositorio.unal.edu.co/handle/unal/80801 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/80801 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
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Atashgar, K. and Zargarabadi, O. Monitoring multivariate pro le data in plastic parts manufacturing industries: An intelligently data processing. Journal of Industrial In- formation Integration, 8:38 { 48, 2017. ISSN 2452-414X. doi: https://doi.org/10. 1016/j.jii.2017.06.003. URL http://www.sciencedirect.com/science/article/pii/ S2452414X16300942. Berrendero, J. R., Justel, A., and Svarc, M. Principal components for multivariate functional data. Computational Statistics & Data Analysis, 55(9):2619{2634, 2011. Boone, J. and Chakraborti, S. Two simple shewhart-type multivariate nonparametric control charts. Applied Stochastic Models in Business and Industry, 28(2):130{140, 2012. Brys, G., Hubert, M., and Rousseeuw, P. J. A robusti cation of independent component analysis. Journal of Chemometrics, 19(5-7):364{375, 2005. doi: https://doi.org/10. 1002/cem.940. URL https://analyticalsciencejournals.onlinelibrary.wiley. com/doi/abs/10.1002/cem.940. Chuang, S. C., Hung, Y. C., Tsai, W.-C., and Yang, S.-F. A framework for nonparametric pro le monitoring. Computers & Industrial Engineering, 64(1):482 { 491, 2013. ISSN 0360-8352. doi: https://doi.org/10.1016/j.cie.2012.08.006. URL http: //www.sciencedirect.com/science/article/pii/S0360835212002057. Claeskens, G., Hubert, M., Slaets, L., and Vakili, K. Multivariate functional halfspace depth. Journal of the American Statistical Association, 109(505):411{423, 2014. doi: 10.1080/01621459.2013.856795. URL https://doi.org/10.1080/01621459. 2013.856795. Crosier, R. B. Multivariate generalizations of cumulative sum quality-control schemes. Technometrics, 30(3):291{303, 1988. Cuevas, A., Febrero, M., and Fraiman, R. On the use of the bootstrap for estimating functions with functional data. Computational Statistics & Data Analysis, 51(2):1063 { 1074, 2006. ISSN 0167-9473. doi: https://doi.org/10.1016/j.csda.2005.10.012. URL http://www.sciencedirect.com/science/article/pii/S0167947305002793. Cuevas, A., Febrero, M., and Fraiman, R. Robust estimation and classi cation for functional data via projection-based depth notions. Computational Statistics, 22(3): 481{496, Sep 2007. ISSN 1613-9658. doi: 10.1007/s00180-007-0053-0. URL https: //doi.org/10.1007/s00180-007-0053-0. Dai, W. and Genton, M. Directional outlyingness for multivariate functional data. Computational Statistics and Data Analysis, 131:50 { 65, 2019. ISSN 0167-9473. doi: https://doi.org/10.1016/j.csda.2018.03.017. URL http://www.sciencedirect. com/science/article/pii/S016794731830077X. High-dimensional and functional data analysis. Fass o, A., Toccu, M., and Magno, M. Functional control charts and health monitoring of steam sterilizers. Quality and Reliability Engineering International, 32(6):2081{2091, 2016. Ferraty, F. and Vieu, P. Nonparametric Functional Data Analysis: Theory and Practice. Springer Series in Statistics. Springer New York, 2010. ISBN 9781441921413. URL https://books.google.com.co/books?id=SlVWcgAACAAJ. Galeano, P., Joseph, E., and Lillo, R. E. The Mahalanobis distance for functional data with applications to classi cation. Technometrics, 57(2):281{291, 2015. Ghosh, M., Li, Y., Zeng, L., Zhang, Z., and Zhou, Q. Modeling multivariate pro les using gaussian process-controlled B-splines. IISE Transactions, 0(0):1{12, 2020. doi: 10.1080/ 24725854.2020.1798038. URL https://doi.org/10.1080/24725854.2020.1798038. G orecki, T., Krzy sko, M., Waszak, L., and Wo ly nski, W. Selected statistical methods of data analysis for multivariate functional data. Statistical Papers, pages 1{30, 2016. Grasso, M., Colosimo, B., and Pacella, M. Pro le monitoring via sensor fusion: The use of PCA methods for multi-channel data. International Journal of Production Research, 02 2014. doi: 10.1080/00207543.2014.916431. Guevara, R. and Vargas, J. Evaluation of process capability in multivariate nonlinear pro les. 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Liu, R. Y., Parelius, J. M., and Singh, K. Multivariate analysis by data depth: descriptive statistics, graphics and inference, (with discussion and a rejoinder by liu and singh). Ann. Statist., 27(3):783{858, 06 1999. doi: 10.1214/aos/1018031260. URL https:// doi.org/10.1214/aos/1018031260. L opez-Pintado, S. and Romo, J. On the concept of depth for functional data. Journal of the American Statistical Association, 104(486):718{734, 2009. doi: 10.1198/jasa.2009.0108. URL https://doi.org/10.1198/jasa.2009.0108. Lowry, C. A., Woodall, W. H., Champ, C. W., and Rigdon, S. E. A multivariate exponentially weighted moving average control chart. Technometrics, 34(1):46{53, 1992. Maleki, M., Amiri, A., and Castagliola, P. An overview on recent pro le monitoring papers (2008-2018) based on conceptual classi cation scheme. Computers & Industrial Engineering, 126:705 { 728, 2018. ISSN 0360-8352. Montgomery, D. C. Introduction to statistical quality control. John Wiley & Sons, 2007. 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Universidad Nacional de Colombia |
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Bogotá - Ciencias - Maestría en Ciencias - Estadística |
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Departamento de Estadística |
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Facultad de Ciencias |
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Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Guevara González, Rubén Darío2db6446b3a559b33e7b356835d8a92f2600Espinosa Moreno, Juan Carlos31643765f4872dd5dac328a7e9c12f216002022-01-11T20:24:29Z2022-01-11T20:24:29Z2021https://repositorio.unal.edu.co/handle/unal/80801Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasEn este trabajo se presentan algunas propuestas para monitorear perfiles no lineales multivariados en fase II, usando métodos provenientes del análisis de datos funcionales. El desempeño de las cartas de control propuestas se evalúa usando simulaciones de Monte Carlo bajo diferentes escenarios. Para ilustrar el uso de la cartas propuestas se presentan ejemplos con datos reales. (Texto tomado de la fuente).In this work, some proposals for the monitoring of multivariate non-linear pro files in phase II will be presented using statistical control charts, using an approach from the Functional Data Analysis. To evaluate the performance of the proposed charts, Monte Carlo simulations will be carried out under different scenarios. To illustrate the use of the proposed letters, examples with real data will be presented.MaestríaMagíster en Ciencias - EstadísticaControl de calidadAnálisis de datos funcionalesxi, 60 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaDepartamento de EstadísticaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasAnálisis de datos funcionalesMétodo de MontecarloFuynctional data analysisMonte-Carlo methodOutlyingnessMonitoringFunctionalProfilesMFPCAClaeskensMahalanobisMonitoreoFuncionalPerfilesAnálisis estadísticoStatistical analysisMonitoreo de perfiles no lineales multivariados usando un enfoque de datos funcionalesMultivariate nonlinear profiles monitoring using a functional data approachTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAtashgar, K. and Zargarabadi, O. Monitoring multivariate pro le data in plastic parts manufacturing industries: An intelligently data processing. Journal of Industrial In- formation Integration, 8:38 { 48, 2017. ISSN 2452-414X. doi: https://doi.org/10. 1016/j.jii.2017.06.003. URL http://www.sciencedirect.com/science/article/pii/ S2452414X16300942.Berrendero, J. R., Justel, A., and Svarc, M. Principal components for multivariate functional data. Computational Statistics & Data Analysis, 55(9):2619{2634, 2011.Boone, J. and Chakraborti, S. Two simple shewhart-type multivariate nonparametric control charts. Applied Stochastic Models in Business and Industry, 28(2):130{140, 2012.Brys, G., Hubert, M., and Rousseeuw, P. J. A robusti cation of independent component analysis. Journal of Chemometrics, 19(5-7):364{375, 2005. doi: https://doi.org/10. 1002/cem.940. URL https://analyticalsciencejournals.onlinelibrary.wiley. com/doi/abs/10.1002/cem.940.Chuang, S. C., Hung, Y. C., Tsai, W.-C., and Yang, S.-F. A framework for nonparametric pro le monitoring. Computers & Industrial Engineering, 64(1):482 { 491, 2013. ISSN 0360-8352. doi: https://doi.org/10.1016/j.cie.2012.08.006. URL http: //www.sciencedirect.com/science/article/pii/S0360835212002057.Claeskens, G., Hubert, M., Slaets, L., and Vakili, K. Multivariate functional halfspace depth. Journal of the American Statistical Association, 109(505):411{423, 2014. doi: 10.1080/01621459.2013.856795. URL https://doi.org/10.1080/01621459. 2013.856795.Crosier, R. B. Multivariate generalizations of cumulative sum quality-control schemes. Technometrics, 30(3):291{303, 1988.Cuevas, A., Febrero, M., and Fraiman, R. On the use of the bootstrap for estimating functions with functional data. Computational Statistics & Data Analysis, 51(2):1063 { 1074, 2006. ISSN 0167-9473. doi: https://doi.org/10.1016/j.csda.2005.10.012. URL http://www.sciencedirect.com/science/article/pii/S0167947305002793.Cuevas, A., Febrero, M., and Fraiman, R. Robust estimation and classi cation for functional data via projection-based depth notions. Computational Statistics, 22(3): 481{496, Sep 2007. ISSN 1613-9658. doi: 10.1007/s00180-007-0053-0. URL https: //doi.org/10.1007/s00180-007-0053-0.Dai, W. and Genton, M. Directional outlyingness for multivariate functional data. Computational Statistics and Data Analysis, 131:50 { 65, 2019. ISSN 0167-9473. doi: https://doi.org/10.1016/j.csda.2018.03.017. URL http://www.sciencedirect. com/science/article/pii/S016794731830077X. High-dimensional and functional data analysis.Fass o, A., Toccu, M., and Magno, M. Functional control charts and health monitoring of steam sterilizers. Quality and Reliability Engineering International, 32(6):2081{2091, 2016.Ferraty, F. and Vieu, P. Nonparametric Functional Data Analysis: Theory and Practice. Springer Series in Statistics. Springer New York, 2010. ISBN 9781441921413. 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