Carta de control multivariada sin distribución para datos funcionales y vectoriales híbridos

ilustraciones, diagramas

Autores:: Rincon Torres, Andrey Duvan

Tipo de recurso:

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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dc.title.spa.fl_str_mv	Carta de control multivariada sin distribución para datos funcionales y vectoriales híbridos
dc.title.translated.eng.fl_str_mv	Distribution-free multivariate control chart for hybrid functional and vector data
title	Carta de control multivariada sin distribución para datos funcionales y vectoriales híbridos
spellingShingle	Carta de control multivariada sin distribución para datos funcionales y vectoriales híbridos 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Control de calidad Datos funcionales Datos funcionales multivariados híbridos Carta de control Componentes principales Componentes principales sensibles Procedimiento no paramétrico Nonparametric procedure Quality control Functional data Hybrid multivariate functional data Control chart Principal components Sensitive principal components Análisis estadístico Análisis multivariado Control de calidad Statistical analysis Multivariate analysis Quality control
title_short	Carta de control multivariada sin distribución para datos funcionales y vectoriales híbridos
title_full	Carta de control multivariada sin distribución para datos funcionales y vectoriales híbridos
title_fullStr	Carta de control multivariada sin distribución para datos funcionales y vectoriales híbridos
title_full_unstemmed	Carta de control multivariada sin distribución para datos funcionales y vectoriales híbridos
title_sort	Carta de control multivariada sin distribución para datos funcionales y vectoriales híbridos
dc.creator.fl_str_mv	Rincon Torres, Andrey Duvan
dc.contributor.advisor.spa.fl_str_mv	Guevara González, Rubèn Darío
dc.contributor.author.spa.fl_str_mv	Rincon Torres, Andrey Duvan
dc.subject.ddc.spa.fl_str_mv	510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
topic	510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Control de calidad Datos funcionales Datos funcionales multivariados híbridos Carta de control Componentes principales Componentes principales sensibles Procedimiento no paramétrico Nonparametric procedure Quality control Functional data Hybrid multivariate functional data Control chart Principal components Sensitive principal components Análisis estadístico Análisis multivariado Control de calidad Statistical analysis Multivariate analysis Quality control
dc.subject.proposal.spa.fl_str_mv	Control de calidad Datos funcionales Datos funcionales multivariados híbridos Carta de control Componentes principales Componentes principales sensibles Procedimiento no paramétrico Nonparametric procedure
dc.subject.proposal.eng.fl_str_mv	Quality control Functional data Hybrid multivariate functional data Control chart Principal components Sensitive principal components
dc.subject.unesco.spa.fl_str_mv	Análisis estadístico Análisis multivariado Control de calidad
dc.subject.unesco.eng.fl_str_mv	Statistical analysis Multivariate analysis Quality control
description	ilustraciones, diagramas
publishDate	2023
dc.date.issued.none.fl_str_mv	2023-11-15
dc.date.accessioned.none.fl_str_mv	2024-01-16T18:56:13Z
dc.date.available.none.fl_str_mv	2024-01-16T18:56:13Z
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
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status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/85334
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/85334 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	Ahsan, Muhammad ; Mashuri, Muhammad ; Kuswanto, Heri ; Prastyo, Dedy D. [u. a.]: Intrusion detection system using multivariate control chart Hotelling’s T2 based on PCA. En: Int. J. Adv. Sci. Eng. Inf. Technol 8 (2018), Nr. 5, p. 1905–1911 Casella, G ; Berger, RL. Statistical inference. vol. 2 Duxbury Pacific Grove. 2002 Chen, Nan ; Zi, Xuemin ; Zou, Changliang: A distribution-free multivariate control chart. En: Technometrics 58 (2016), Nr. 4, p. 448–459 Chen, Q. ; Kruger, U. ; Meronk, M. ; Leung, A.Y.T.: Synthesis of T2 and Q statistics for process monitoring. En: Control Engineering Practice 12 (2004), Nr. 6, p. 745–755 Fan, Shu-Kai S. ; Jen, Chih-Hung ; Lee, Tzu-Yi: Modeling and monitoring the nonlinear profile of heat treatment process data by using an approach based on a hyperbolic tangent function. En: Quality Engineering 29 (2017), Nr. 2, p. 226–243 Ferraty, Frédéric ; Vieu, Philippe: Nonparametric functional data analysis: theory and practice. Vol. 76. Springer, 2006 Ghashghaei, Reza ; Amiri, Amirhossein ; Khosravi, Peyman: New control charts for simultaneous monitoring of the mean vector and covariance matrix of multivariate multiple linear profiles. En: Communications in Statistics-Simulation and Computation 48 (2019), Nr. 5, p. 1382–1405 Gramacki, Artur: Nonparametric kernel density estimation and its computational aspects. Vol. 37. Springer, 2018 Happ, Clara ; Greven, Sonja: Multivariate functional principal component analysis for data observed on different (dimensional) domains. En: Journal of the American Statistical Association 113 (2018), Nr. 522, p. 649–659 Happ-Kurz, Clara: Object-Oriented Software for Functional Data. En: Journal of Statistical Software 93 (2020), Nr. 5, p. 1–38 Harezlak, Jaroslaw ; Ruppert, David ; Wand, Matt P.: Semiparametric regression with R. Vol. 109. Springer, 2018 Horváth, Lajos ; Kokoszka, Piotr: Inference for functional data with applications. Vol. 200. Springer Science & Business Media, 2012 Huang, Wei-Heng ; Sun, Jing ; Yeh, Arthur B.: Monitoring and diagnostics of correlated quality variables of different types. En: Journal of Quality Technology 55 (2023), Nr. 2, p. 220–252 Hung, Ying-Chao ; Tsai,Wen-Chi ; Yang, Su-Fen ; Chuang, Shih-Chung ; Tseng, Yi-Kuan: Nonparametric profile monitoring in multi-dimensional data spaces. En: Journal of Process Control 22 (2012), Nr. 2, p. 397–403 Jang, Jeong H.: Principal component analysis of hybrid functional and vector data. En: Statistics in medicine 40 (2021), Nr. 24, p. 5152–5173 Jensen, Willis A. ; Birch, Jeffrey B. ; Woodall, William H.: Monitoring correlation within linear profiles using mixed models. En: Journal of Quality Technology 40 (2008), Nr. 2, p. 167–183 Jiang, Qingchao ; Yan, Xuefeng ; Zhao, Weixiang: Fault detection and diagnosis in chemical processes using sensitive principal component analysis. En: Industrial & Engineering Chemistry Research 52 (2013), Nr. 4, p. 1635–1644 Lei, Yong ; Zhang, Zhisheng ; Jin, Jionghua: Automatic tonnage monitoring for missing part detection in multi-operation forging processes. En: Journal of manufacturing science and engineering 132 (2010), Nr. 5 Maleki, Mohammad R. ; Amiri, Amirhossein ; Castagliola, Philippe: An overview on recent profile monitoring papers (2008-2018) based on conceptual classification scheme. En: Computers & Industrial Engineering 126 (2018), p. 705–728 Montgomery, Douglas C.: Introduction to statistical quality control. John Wiley & Sons, 2020 Niaki, Seyed Taghi A. ; Abbasi, Babak: Fault diagnosis in multivariate control charts using artificial neural networks. En: Quality and reliability engineering international 21 (2005), Nr. 8, p. 825–840 Noorossana, Rassoul ; Eyvazian, M ; Vaghefi, A: Phase II monitoring of multivariate simple linear profiles. En: Computers & Industrial Engineering 58 (2010), Nr. 4, p. 563–570 Pan, Jeh-Nan ; Li, Chung-I ; Lu, Meng Z.: Detecting the process changes for multivariate nonlinear profile data. En: Quality and Reliability Engineering International 35 (2019), Nr. 6, p. 1890–1910 Paynabar, Kamran ; Jin, Jionghua ; Agapiou, John ; Deeds, Paula: Robust leak tests for transmission systems using nonlinear mixed-effect models. En: Journal of quality technology 44 (2012), Nr. 3, p. 265–278 Paynabar, Kamran ; Jin, Jionghua ; Pacella, Massimo: Monitoring and diagnosis of multichannel nonlinear profile variations using uncorrelated multilinear principal component analysis. En: Iie transactions 45 (2013), Nr. 11, p. 1235–1247 Paynabar, Kamran ; Zou, Changliang ; Qiu, Peihua: A change-point approach for phase-I analysis in multivariate profile monitoring and diagnosis. En: Technometrics 58 (2016), Nr. 2, p. 191–204 Qi, Dequan ;Wang, Zhaojun ; Zi, Xuemin ; Li, Zhonghua: Phase II monitoring of generalized linear profiles using weighted likelihood ratio charts. En: Computers & Industrial Engineering 94 (2016), p. 178–187 Qiu, Peihua: Introduction to statistical process control. CRC press, 2013 Ren, Haojie ; Chen, Nan ; Wang, Zhaojun: Phase-II monitoring in multichannel profile observations. En: Journal of Quality Technology 51 (2019), Nr. 4, p. 338 352 Research, Eigenvector: NIR of Corn Samples for Standardization Benchmarking. (2005) Rizzo, Caterina ; Chin, Swee-Teng ; van den Heuvel, Edwin ; Di Bucchianico, Alessandro: Performance measures of discrete and continuous time-between-events control charts. En: Quality and Reliability Engineering International 36 (2020), Nr. 8, p. 2754–2768 Ryan, Thomas P.: Statistical methods for quality improvement. John Wiley & Sons, 2011 Shams, MA B. ; Budman, HM ; Duever, TA: Fault detection, identification and diagnosis using CUSUM based PCA. En: Chemical Engineering Science 66 (2011), Nr. 20, p. 4488–4498 Soleimani, Paria ; Noorossana, Rassoul ; Niaki, STA: Monitoring autocorrelated multivariate simple linear profiles. En: The International Journal of Advanced Manufacturing Technology 67 (2013), Nr. 5, p. 1857–1865 Williams, James D. ; Woodall, William H. ; Birch, Jeffrey B.: Statistical monitoring of nonlinear product and process quality profiles. En: Quality and Reliability Engineering International 23 (2007), Nr. 8, p. 925–941 Wold, Svante ; Esbensen, Kim ; Geladi, Paul: Principal component analysis. En: Chemometrics and intelligent laboratory systems 2 (1987), Nr. 1-3, p. 37–52 Yang, Zhongfu ; Nie, Gang ; Pan, Ling ; Zhang, Yan ; Huang, Linkai ; Ma, Xiao ; Zhang, Xinquan: Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum. En: PeerJ 5 (2017), p. e3867 Zhang, Jiajia ; Ren, Haojie ; Yao, Rui ; Zou, Changliang ; Wang, Zhaojun: Phase I analysis of multivariate profiles based on regression adjustment. En: Computers & Industrial Engineering 85 (2015), p. 132–144 Zhou, Qin ; Zou, Changliang ; Wang, Zhaojun ; Jiang, Wei: Likelihood-based EWMA charts for monitoring Poisson count data with time-varying sample sizes. En: Journal of the American Statistical Association 107 (2012), Nr. 499, p. 1049–1062 Zou, Changliang ; Tsung, Fugee ; Wang, Zhaojun: Monitoring general linear profiles using multivariate exponentially weighted moving average schemes. En: Technometrics 49 (2007), Nr. 4, p. 395–408 Zou, Changliang ; Tsung, Fugee ; Wang, Zhaojun: Monitoring profiles based on nonparametric regression methods. En: Technometrics 50 (2008), Nr. 4, p. 512–526
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dc.rights.license.spa.fl_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.format.extent.spa.fl_str_mv	xii, 66 páginas
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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.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	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Guevara González, Rubèn Darío2db6446b3a559b33e7b356835d8a92f2600Rincon Torres, Andrey Duvan564e42634151c3f1ce6c5c8246e3cfbf2024-01-16T18:56:13Z2024-01-16T18:56:13Z2023-11-15https://repositorio.unal.edu.co/handle/unal/85334Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasEsta tesis propone una metodología de monitoreo en Fase II para procesos funcionales multivariados híbridos, que combinan una parte funcional y una parte vectorial multivariada. La Metodología emplea una carta de control que tiene en cuenta la correlación entre funciones y vectores, y se fundamenta en el análisis de componentes principales, híbridos y componentes sensibles. Mediante simulaciones, se evidencia la efectividad de la metodología para detectar cambios de distintas magnitudes en diferentes escenarios, tales como distribuciones, tamaños de muestra y configuraciones de media fuera de control. Además, se muestra que la metodología es más eficiente que el seguimiento por separado de las partes funcional y vectorial. Finalmente, se ilustra la aplicación de la metodología a casos reales de producción de maíz y ray-grass italiano, demostrando su utilidad para el control de calidad en la producción agrícola. (Texto tomado de la fuente).This thesis proposes a Phase II monitoring methodology for hybrid multivariate functional processes that combine a functional component and a multivariate vector component. The methodology employs a control chart that takes into account the correlation between functions and vectors, and it is grounded in the analysis of hybrid principal components and sensitive components. Through simulations, the effectiveness of the methodology in detecting changes of various magnitudes in different scenarios, such as distributions, sample sizes, and out-of-control mean configurations, is demonstrated. Furthermore, it is shown that the methodology is more efficient than separately monitoring the functional and vectorial components. Finally, the application of the methodology to real cases of corn and Italian ryegrass production is illustrated, demonstrating its utility for quality control in agricultural production.MaestríaMagíster en Ciencias - EstadísticaControl estadístico de calidad, análisis de datos funcionalesxii, 66 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasControl de calidadDatos funcionalesDatos funcionales multivariados híbridosCarta de controlComponentes principalesComponentes principales sensiblesProcedimiento no paramétricoNonparametric procedureQuality controlFunctional dataHybrid multivariate functional dataControl chartPrincipal componentsSensitive principal componentsAnálisis estadísticoAnálisis multivariadoControl de calidadStatistical analysisMultivariate analysisQuality controlCarta de control multivariada sin distribución para datos funcionales y vectoriales híbridosDistribution-free multivariate control chart for hybrid functional and vector dataTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAhsan, Muhammad ; Mashuri, Muhammad ; Kuswanto, Heri ; Prastyo, Dedy D. [u. a.]: Intrusion detection system using multivariate control chart Hotelling’s T2 based on PCA. En: Int. J. Adv. Sci. Eng. Inf. Technol 8 (2018), Nr. 5, p. 1905–1911Casella, G ; Berger, RL. Statistical inference. vol. 2 Duxbury Pacific Grove. 2002Chen, Nan ; Zi, Xuemin ; Zou, Changliang: A distribution-free multivariate control chart. En: Technometrics 58 (2016), Nr. 4, p. 448–459Chen, Q. ; Kruger, U. ; Meronk, M. ; Leung, A.Y.T.: Synthesis of T2 and Q statistics for process monitoring. En: Control Engineering Practice 12 (2004), Nr. 6, p. 745–755Fan, Shu-Kai S. ; Jen, Chih-Hung ; Lee, Tzu-Yi: Modeling and monitoring the nonlinear profile of heat treatment process data by using an approach based on a hyperbolic tangent function. En: Quality Engineering 29 (2017), Nr. 2, p. 226–243Ferraty, Frédéric ; Vieu, Philippe: Nonparametric functional data analysis: theory and practice. Vol. 76. Springer, 2006Ghashghaei, Reza ; Amiri, Amirhossein ; Khosravi, Peyman: New control charts for simultaneous monitoring of the mean vector and covariance matrix of multivariate multiple linear profiles. En: Communications in Statistics-Simulation and Computation 48 (2019), Nr. 5, p. 1382–1405Gramacki, Artur: Nonparametric kernel density estimation and its computational aspects. Vol. 37. Springer, 2018Happ, Clara ; Greven, Sonja: Multivariate functional principal component analysis for data observed on different (dimensional) domains. En: Journal of the American Statistical Association 113 (2018), Nr. 522, p. 649–659Happ-Kurz, Clara: Object-Oriented Software for Functional Data. En: Journal of Statistical Software 93 (2020), Nr. 5, p. 1–38Harezlak, Jaroslaw ; Ruppert, David ; Wand, Matt P.: Semiparametric regression with R. Vol. 109. Springer, 2018Horváth, Lajos ; Kokoszka, Piotr: Inference for functional data with applications. Vol. 200. Springer Science & Business Media, 2012Huang, Wei-Heng ; Sun, Jing ; Yeh, Arthur B.: Monitoring and diagnostics of correlated quality variables of different types. En: Journal of Quality Technology 55 (2023), Nr. 2, p. 220–252Hung, Ying-Chao ; Tsai,Wen-Chi ; Yang, Su-Fen ; Chuang, Shih-Chung ; Tseng, Yi-Kuan: Nonparametric profile monitoring in multi-dimensional data spaces. En: Journal of Process Control 22 (2012), Nr. 2, p. 397–403Jang, Jeong H.: Principal component analysis of hybrid functional and vector data. En: Statistics in medicine 40 (2021), Nr. 24, p. 5152–5173Jensen, Willis A. ; Birch, Jeffrey B. ; Woodall, William H.: Monitoring correlation within linear profiles using mixed models. En: Journal of Quality Technology 40 (2008), Nr. 2, p. 167–183Jiang, Qingchao ; Yan, Xuefeng ; Zhao, Weixiang: Fault detection and diagnosis in chemical processes using sensitive principal component analysis. En: Industrial & Engineering Chemistry Research 52 (2013), Nr. 4, p. 1635–1644Lei, Yong ; Zhang, Zhisheng ; Jin, Jionghua: Automatic tonnage monitoring for missing part detection in multi-operation forging processes. En: Journal of manufacturing science and engineering 132 (2010), Nr. 5Maleki, Mohammad R. ; Amiri, Amirhossein ; Castagliola, Philippe: An overview on recent profile monitoring papers (2008-2018) based on conceptual classification scheme. En: Computers & Industrial Engineering 126 (2018), p. 705–728Montgomery, Douglas C.: Introduction to statistical quality control. John Wiley & Sons, 2020Niaki, Seyed Taghi A. ; Abbasi, Babak: Fault diagnosis in multivariate control charts using artificial neural networks. En: Quality and reliability engineering international 21 (2005), Nr. 8, p. 825–840Noorossana, Rassoul ; Eyvazian, M ; Vaghefi, A: Phase II monitoring of multivariate simple linear profiles. En: Computers & Industrial Engineering 58 (2010), Nr. 4, p. 563–570Pan, Jeh-Nan ; Li, Chung-I ; Lu, Meng Z.: Detecting the process changes for multivariate nonlinear profile data. En: Quality and Reliability Engineering International 35 (2019), Nr. 6, p. 1890–1910Paynabar, Kamran ; Jin, Jionghua ; Agapiou, John ; Deeds, Paula: Robust leak tests for transmission systems using nonlinear mixed-effect models. En: Journal of quality technology 44 (2012), Nr. 3, p. 265–278Paynabar, Kamran ; Jin, Jionghua ; Pacella, Massimo: Monitoring and diagnosis of multichannel nonlinear profile variations using uncorrelated multilinear principal component analysis. En: Iie transactions 45 (2013), Nr. 11, p. 1235–1247Paynabar, Kamran ; Zou, Changliang ; Qiu, Peihua: A change-point approach for phase-I analysis in multivariate profile monitoring and diagnosis. En: Technometrics 58 (2016), Nr. 2, p. 191–204Qi, Dequan ;Wang, Zhaojun ; Zi, Xuemin ; Li, Zhonghua: Phase II monitoring of generalized linear profiles using weighted likelihood ratio charts. En: Computers & Industrial Engineering 94 (2016), p. 178–187Qiu, Peihua: Introduction to statistical process control. CRC press, 2013Ren, Haojie ; Chen, Nan ; Wang, Zhaojun: Phase-II monitoring in multichannel profile observations. En: Journal of Quality Technology 51 (2019), Nr. 4, p. 338 352Research, Eigenvector: NIR of Corn Samples for Standardization Benchmarking. (2005)Rizzo, Caterina ; Chin, Swee-Teng ; van den Heuvel, Edwin ; Di Bucchianico, Alessandro: Performance measures of discrete and continuous time-between-events control charts. En: Quality and Reliability Engineering International 36 (2020), Nr. 8, p. 2754–2768Ryan, Thomas P.: Statistical methods for quality improvement. John Wiley & Sons, 2011Shams, MA B. ; Budman, HM ; Duever, TA: Fault detection, identification and diagnosis using CUSUM based PCA. En: Chemical Engineering Science 66 (2011), Nr. 20, p. 4488–4498Soleimani, Paria ; Noorossana, Rassoul ; Niaki, STA: Monitoring autocorrelated multivariate simple linear profiles. En: The International Journal of Advanced Manufacturing Technology 67 (2013), Nr. 5, p. 1857–1865Williams, James D. ; Woodall, William H. ; Birch, Jeffrey B.: Statistical monitoring of nonlinear product and process quality profiles. En: Quality and Reliability Engineering International 23 (2007), Nr. 8, p. 925–941Wold, Svante ; Esbensen, Kim ; Geladi, Paul: Principal component analysis. En: Chemometrics and intelligent laboratory systems 2 (1987), Nr. 1-3, p. 37–52Yang, Zhongfu ; Nie, Gang ; Pan, Ling ; Zhang, Yan ; Huang, Linkai ; Ma, Xiao ; Zhang, Xinquan: Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum. En: PeerJ 5 (2017), p. e3867Zhang, Jiajia ; Ren, Haojie ; Yao, Rui ; Zou, Changliang ; Wang, Zhaojun: Phase I analysis of multivariate profiles based on regression adjustment. En: Computers & Industrial Engineering 85 (2015), p. 132–144Zhou, Qin ; Zou, Changliang ; Wang, Zhaojun ; Jiang, Wei: Likelihood-based EWMA charts for monitoring Poisson count data with time-varying sample sizes. En: Journal of the American Statistical Association 107 (2012), Nr. 499, p. 1049–1062Zou, Changliang ; Tsung, Fugee ; Wang, Zhaojun: Monitoring general linear profiles using multivariate exponentially weighted moving average schemes. En: Technometrics 49 (2007), Nr. 4, p. 395–408Zou, Changliang ; Tsung, Fugee ; Wang, Zhaojun: Monitoring profiles based on nonparametric regression methods. En: Technometrics 50 (2008), Nr. 4, p. 512–526EstudiantesInvestigadoresMaestrosPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/85334/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1116553091.2023.pdf1116553091.2023.pdfTesis de Maestría en Ciencias - Estadísticaapplication/pdf6581162https://repositorio.unal.edu.co/bitstream/unal/85334/2/1116553091.2023.pdfdfce42dcad3cc28c8a0d0e839525b510MD52THUMBNAIL1116553091.2023.pdf.jpg1116553091.2023.pdf.jpgGenerated Thumbnailimage/jpeg4358https://repositorio.unal.edu.co/bitstream/unal/85334/3/1116553091.2023.pdf.jpg523bc45f144575eace7545366925348cMD53unal/85334oai:repositorio.unal.edu.co:unal/853342024-01-16 23:03:38.549Repositorio Institucional Universidad Nacional de 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