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dc.contributor.advisorGonzález-Barreto, David
dc.contributor.authorOtero-Padilla, Vivian
dc.date.accessioned2018-11-28T17:10:52Z
dc.date.available2018-11-28T17:10:52Z
dc.date.issued2005
dc.identifier.urihttps://hdl.handle.net/20.500.11801/1537
dc.description.abstractProcess Oriented Basis Representations (POBREP) is a multivariate Statistical Process Control (SPC) procedure with diagnosis capabilities developed by Barton and Gonzalez-Barreto (1996). Although this methodology is effective when orthogonal process-oriented basis (POB) is presented, it is diagnosis capabilities are at risk when the POB is not orthogonal. This research compared several methods to solve non-orthogonal POB’s problem. Six scenarios with different Variance Inflation Factor (VIF) severity were created using the stencil printing process. Coefficients were estimated using five methods: Ordinary Least Square (OLS), Independent Subsets (IS), Simple Regression (SR), Ridge Regression (RR) and Constrained Solution Space (CSS). These methods were compared in terms of the lower Square Error (SE) and higher number of times the coefficient is between a confidence interval (Count). There were two comparable groups of results: (1) CSS and RR methods with lowest SE and highest Count and (2) OLS, IS and SR with higher SE and lower Count. The best method estimate POBREP coefficient in presence of non non-orthogonal basis elements is Constraint Space Solution.en_US
dc.description.abstractRepresentación de las Bases Orientadas al Proceso (POBREP) es una metodología de análisis multivariado desarrollada por Barton y González-Barreto (1996) que tiene la capacidad de diagnóstico. Esta metodología es efectiva cuando las bases orientadas al proceso (POB) son ortogonales, pero esta capacidad de diagnóstico se afecta cuando los POBs no son ortogonales. Esta investigación compara varios métodos que permiten resolver el problema de falta de ortogonalidad en los POBs. Seis escenarios con diferentes severidades de VIF fueron desarrollados utilizando el proceso de impresión de un esténcil. Los coeficientes fueron estimados usando cinco métodos: Minimizar Errores Cuadraros (OLS), Subgrupos Independientes (IS), Regresión Simple (SR), Regresión “Ridge” (RR) y Solución de Espacio Limitado (CSS). Estos métodos fueron comparados con el objetivo de minimizar los errores cuadrados (SE) y maximizar el numero de veces que el coeficiente se encuentran entre unos limites de confianza (“Count”). Hay dos grupos de resultados comparables: (1) CSS y RR con valores mínimos de SE y valores altos “Count” ,(2) OLS, IS y SR obtuvieron valores altos de SE y bajos de “Count”. El método que mejor estima los coeficientes de POBREP en presencia de falta de ortogonalidad en los elementos de la base es Solución de Espacio Limitado.en_US
dc.language.isoEnglishen_US
dc.subjectProcess Oriented Basis Representationsen
dc.subjectMultivariate Statistical Process Controlen
dc.subjectOrthogonal process-oriented basisen
dc.subjectNon-orthogonalen
dc.titleProcess oriented basis estimation in presence of non-orthogonal basis elementsen_US
dc.typeThesisen_US
dc.rights.licenseAll rights reserveden_US
dc.rights.holder(c)2005 Vivian Otero Padillaen_US
dc.contributor.committeeDeliz, José R.
dc.contributor.committeeResto, Pedro
dc.contributor.representativeMedina, Maria
thesis.degree.levelM.S.en_US
thesis.degree.disciplineIndustrial Engineeringen_US
dc.contributor.collegeCollege of Engineeringen_US
dc.contributor.departmentDepartment of Industrial Engineeringen_US
dc.description.graduationYear2005en_US


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