Monitoreo de perfiles no lineales multivariados usando un enfoque de datos funcionales

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dc.contributor.advisorGuevara González, Rubén Daríospa
dc.contributor.authorEspinosa Moreno, Juan Carlosspa
dc.date.accessioned2022-01-11T20:24:29Z
dc.date.available2022-01-11T20:24:29Z
dc.date.issued2021
dc.descriptionilustraciones, gráficas, tablasspa
dc.description.abstractEn 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).spa
dc.description.abstractIn 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.eng
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ciencias - Estadísticaspa
dc.description.researchareaControl de calidadspa
dc.description.researchareaAnálisis de datos funcionalesspa
dc.format.extentxi, 60 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/80801
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.departmentDepartamento de Estadísticaspa
dc.publisher.facultyFacultad de Cienciasspa
dc.publisher.placeBogotá, Colombiaspa
dc.publisher.programBogotá - Ciencias - Maestría en Ciencias - Estadísticaspa
dc.relation.referencesAtashgar, 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.spa
dc.relation.referencesBerrendero, J. R., Justel, A., and Svarc, M. Principal components for multivariate functional data. Computational Statistics & Data Analysis, 55(9):2619{2634, 2011.spa
dc.relation.referencesBoone, J. and Chakraborti, S. Two simple shewhart-type multivariate nonparametric control charts. Applied Stochastic Models in Business and Industry, 28(2):130{140, 2012.spa
dc.relation.referencesBrys, 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.spa
dc.relation.referencesChuang, 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.spa
dc.relation.referencesClaeskens, 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.spa
dc.relation.referencesCrosier, R. B. Multivariate generalizations of cumulative sum quality-control schemes. Technometrics, 30(3):291{303, 1988.spa
dc.relation.referencesCuevas, 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.spa
dc.relation.referencesCuevas, 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.spa
dc.relation.referencesDai, 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.spa
dc.relation.referencesFass 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.spa
dc.relation.referencesFerraty, 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.spa
dc.relation.referencesGaleano, P., Joseph, E., and Lillo, R. E. The Mahalanobis distance for functional data with applications to classi cation. Technometrics, 57(2):281{291, 2015.spa
dc.relation.referencesGhosh, 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.spa
dc.relation.referencesG 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.spa
dc.relation.referencesGrasso, 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.spa
dc.relation.referencesGuevara, R. and Vargas, J. Evaluation of process capability in multivariate nonlinear pro les. Journal of Statistical Computation and Simulation, 86(12):2411{2428, 2016.spa
dc.relation.referencesHapp, C. and Greven, S. Multivariate functional principal component analysis for data observed on di erent (dimensional) domains. Journal of the American Statisti- cal Association, 113(522):649{659, 2018. doi: 10.1080/01621459.2016.1273115. URL https://doi.org/10.1080/01621459.2016.1273115.spa
dc.relation.referencesHorv ath, L. and Kokoszka, P. Inference for Functional Data with Applications. Springer Series in Statistics. Springer New York, 2012. ISBN 9781461436546. URL https: //books.google.com.co/books?id=PGOCtgAACAAJ.spa
dc.relation.referencesHubert, M. Data depth: Robust multivariate analysis, computational geometry and applications by Liu, R., Ser ing, R., and Souvaine, D. L. Biometrics, 64(2):655{656, 2008. doi: 10.1111/j.1541-0420.2008.01026n 6.x. URL https://onlinelibrary.wiley.com/ doi/abs/10.1111/j.1541-0420.2008.01026_6.x.spa
dc.relation.referencesHubert, M., Rousseeuw, P., and Segaert, P. Multivariate functional outlier detection. Statistical Methods & Applications, 24:177{202, 2015.spa
dc.relation.referencesHubert, M., Rousseeuw, P., and Segaert, P. Multivariate and functional classi cation using depth and distance. Advances in Data Analysis and Classi cation, 11(3):445{466, Sep 2017. ISSN 1862-5355. doi: 10.1007/s11634-016-0269-3. URL https://doi.org/10. 1007/s11634-016-0269-3.spa
dc.relation.referencesIeva, F. and Paganoni, A. M. Depth measures for multivariate functional data. Com- munications in Statistics - Theory and Methods, 42(7):1265{1276, 2013. doi: 10.1080/ 03610926.2012.746368. URL https://doi.org/10.1080/03610926.2012.746368.spa
dc.relation.referencesJacques, J. and Preda, C. Model-based clustering for multivariate functional data. Compu- tational Statistics & Data Analysis, 71:92{106, 2014.spa
dc.relation.referencesJahani, S., Kontar, R., Veeramani, D., and Zhou, S. Statistical monitoring of multiple pro les simultaneously using gaussian processes. Quality and Reliability Engi- neering International, 34(8):1510{1529, 2018. doi: 10.1002/qre.2326. URL https: //onlinelibrary.wiley.com/doi/abs/10.1002/qre.2326.spa
dc.relation.referencesJones-Farmer, L. A., Woodall, W. H., Steiner, S. H., and Champ, C. W. An overview of Phase I analysis for process improvement and monitoring. Journal of Qua- lity Technology, 46(3):265{280, 2014. doi: 10.1080/00224065.2014.11917969. URL https://doi.org/10.1080/00224065.2014.11917969.spa
dc.relation.referencesKang, L. and Albin, S. L. On-line monitoring when the process yields a linear pro le. Journal of Quality Technology, 32(4):418, 2000.spa
dc.relation.referencesKokoszka, P. and Reimherrm, M. Introduction to Functional Data Analysis. Chapman & Hall / CRC numerical analysis and scienti c computing. CRC Press, 2017. ISBN 9781498746342. URL https://books.google.com.co/books?id=HIxIvgAACAAJ.spa
dc.relation.referencesLi, Z., Dai, Y., and Wang, Z. Multivariate change point control chart based on data depth for Phase I analysis. Communications in Statistics - Simulation and Computation, 43 (6):1490{1507, 2014. doi: 10.1080/03610918.2012.735319. URL https://doi.org/10. 1080/03610918.2012.735319.spa
dc.relation.referencesLiu, 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.spa
dc.relation.referencesL 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.spa
dc.relation.referencesLowry, 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.spa
dc.relation.referencesMaleki, 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.spa
dc.relation.referencesMontgomery, D. C. Introduction to statistical quality control. John Wiley & Sons, 2007.spa
dc.relation.referencesMunck, L., Norgaard, L., Engelsen, S., Bro, R., and Andersson, C. Chemometrics in food science-a demonstration of the feasibility of a highly exploratory, inductive evaluation strategy of fundamental scienti c signi cance. Chemometrics and Intelligent Laboratory Systems, 44(1):31{60, 1998. ISSN 0169-7439. doi: https://doi.org/10.1016/ S0169-7439(98)00074-4. URL https://www.sciencedirect.com/science/article/ pii/S0169743998000744.spa
dc.relation.referencesNoorossana, R., Saghaei, A., and Amiri, A. Statistical analysis of pro le monitoring, volume 865. John Wiley & Sons, 2011.spa
dc.relation.referencesPan, J.-N., Li, C.-I., and Lu, M. Z. Detecting the process changes for multivariate nonlinear pro le data. Quality and Reliability Engineering International, 35(6):1890{1910, 2019. doi: 10.1002/qre.2482. URL https://onlinelibrary.wiley.com/doi/abs/10.1002/ qre.2482.spa
dc.relation.referencesPaynabar, K., Zou, C., and Qiu, P. A change-point approach for Phase-I analysis in multivariate pro le monitoring and diagnosis. Technometrics, 58(2):191{204, 2016. doi: 10.1080/00401706.2015.1042168. URL https://doi.org/10.1080/00401706.2015. 1042168.spa
dc.relation.referencesPe~na, D. An alisis multivariante de datos. McGraw-Hill Interamericana de Espa~na S.L., 2002. ISBN 9788448136109.spa
dc.relation.referencesPignatiello, J. J. and Runger, G. C. Comparisons of multivariate cusum charts. Journal of quality technology, 22(3):173{186, 1990.spa
dc.relation.referencesQiu, P. Introduction to statistical process control. CRC Press, 2013.spa
dc.relation.referencesQiu, P., Zou, C., and Wang, Z. Nonparametric pro le monitoring by mixed e ects modeling. Technometrics, 52(3):265{277, 2010.spa
dc.relation.referencesR Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2016. URL https://www.R-project.org/.spa
dc.relation.referencesRamsay, J. and Silverman, B. Functional data analysis. Springer Series in Statistics. Springer, 2ed edition, 2005. ISBN 038740080X,9780387400808.spa
dc.relation.referencesSchabenberger, O. and Pierce, F. J. Contemporary statistical models for the plant and soil sciences. CRC press, 2001.spa
dc.relation.referencesSheu, S. H., Ouyoung, C. W., and Hsu, T. S. Phase II statistical process control for functional data. Journal of Statistical Computation and Simulation, 83(11):2144{2159, 2013.spa
dc.relation.referencesShiau, J.-J. H., Huang, H.-L., Lin, S.-H., and Tsai, M.-Y. Monitoring nonlinear pro les with random e ects by nonparametric regression. Communications in Statistics Theory and Methods, 38(10):1664{1679, 2009.spa
dc.relation.referencesTarabelloni, N., Biasi, R., Paganoni, A., and Ieva, F. Multivariate functional data depth measure based on variance-covariance operators. 07 2014.spa
dc.relation.referencesWang, K. and Tsung, F. Hierarchical sparse functional principal component analysis for multistage multivariate pro le data. IISE Transactions, 0(0):1{16, 2020. doi: 10.1080/ 24725854.2020.1738599. URL https://doi.org/10.1080/24725854.2020.1738599.spa
dc.relation.referencesWang, Y., Mei, Y., and Paynabar, K. Thresholded multivariate principal component analysis for Phase I multichannel pro le monitoring. Technometrics, 0(0):1{13, 2018. doi: 10.1080/00401706.2017.1375993. URL https://doi.org/10.1080/00401706.2017. 1375993.spa
dc.relation.referencesWilliams, J. D., Woodall, W. H., and Birch, J. B. Statistical monitoring of nonlinear product and process quality pro les. Quality and Reliability Engineering International, 23(8):925{941, 2007.spa
dc.relation.referencesYeh, A. B., Huwang, L., and Li, Y.-M. Pro le monitoring for a binary response. IIE Transactions, 41(11):931{941, 2009.spa
dc.relation.referencesZhang, C., Yan, H., Lee, S., and Shi, J. Weakly correlated pro le monitoring based on sparse multi-channel functional principal component analysis. IISE Transactions, 50 (10):878{891, 2018. doi: 10.1080/24725854.2018.1451012. URL https://doi.org/10. 1080/24725854.2018.1451012spa
dc.relation.referencesZhang, J., Ren, H., Yao, R., Zou, C., and Wang, Z. Phase I analysis of multivariate pro les based on regression adjustment. Comput. Ind. Eng., 85(C):132{144, July 2015. ISSN 0360-8352. doi: 10.1016/j.cie.2015.02.025. URL http://dx.doi.org/10.1016/j.cie. 2015.02.025.spa
dc.relation.referencesZou, C., Tsung, F., and Wang, Z. Monitoring pro les based on nonparametric regression methods. Technometrics, 50(4):512{526, 2008. doi: 10.1198/004017008000000433. URL https://doi.org/10.1198/004017008000000433.spa
dc.relation.referencesZou, C., Wang, Z., and Tsung, F. A spatial rank-based multivariate EWMA control chart. Naval Research Logistics (NRL), 59(2):91{110, 2012.spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseReconocimiento 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/spa
dc.subject.ddc510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasspa
dc.subject.lembAnálisis de datos funcionalesspa
dc.subject.lembFuynctional data analysiseng
dc.subject.lembMétodo de Montecarlospa
dc.subject.lembMonte-Carlo methodeng
dc.subject.proposalOutlyingnesseng
dc.subject.proposalMahalanobisspa
dc.subject.proposalMonitoringeng
dc.subject.proposalMonitoreospa
dc.subject.proposalFunctionaleng
dc.subject.proposalFuncionalspa
dc.subject.proposalPerfilesspa
dc.subject.proposalProfileseng
dc.subject.proposalMFPCAeng
dc.subject.proposalClaeskenseng
dc.subject.unescoAnálisis estadísticospa
dc.subject.unescoStatistical analysiseng
dc.titleMonitoreo de perfiles no lineales multivariados usando un enfoque de datos funcionalesspa
dc.title.translatedMultivariate nonlinear profiles monitoring using a functional data approacheng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentPúblico generalspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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