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Sinopsis
This book presents a new augmentation method to eliminate the multicollinearity in datasets that contain several correlated predictor variables. The objective in mind is to reduce the estimation error of the regression coefficients. The main contribution of this work consists in offering a new alternative to eliminate multicollinearity in datasets by using small runs which are added in a sequential manner. The algorithm proposed will indicate the point in which the augmentations have sufficiently contributed to find the true regression model. The procedure is based on addition of new observations to the point in which an appropriate regression model can be constructed. The new information is obtained through designed experiments using the R3 algorithm as a guideline to perform the augmentations and the Ridge Trace and VIF statistic as verification tools that help to determine the point in which the correlations have been significantly reduced. The final result is a linear regression model that accurately represents the process under study.
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M.I.E. Russell Pérez González received his I.E. from Instituto de Tuxtla Gutiérrez, Chiapas and his Masters from Technological Institute of Celaya, Guanajuato. M.I.E. Russell´s degrees are all in Industrial Engineering. His concentration area includes experimental design, mechanical design and technology management.
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A revision on Multicollinearity and Augmentation Methods
Publicado por
LAP LAMBERT Academic Publishing Jan 2014, 2014
ISBN 10: 3659507032
ISBN 13: 9783659507038
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a new augmentation method to eliminate the multicollinearity in datasets that contain several correlated predictor variables. The objective in mind is to reduce the estimation error of the regression coefficients. The main contribution of this work consists in offering a new alternative to eliminate multicollinearity in datasets by using small runs which are added in a sequential manner. The algorithm proposed will indicate the point in which the augmentations have sufficiently contributed to find the true regression model. The procedure is based on addition of new observations to the point in which an appropriate regression model can be constructed. The new information is obtained through designed experiments using the R3 algorithm as a guideline to perform the augmentations and the Ridge Trace and VIF statistic as verification tools that help to determine the point in which the correlations have been significantly reduced. The final result is a linear regression model that accurately represents the process under study. 76 pp. Englisch. Nº de ref. del artículo: 9783659507038
Cantidad disponible: 2 disponibles
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A revision on Multicollinearity and Augmentation Methods : Multicollinearity and Augmentation Methods
Publicado por
LAP LAMBERT Academic Publishing, 2014
ISBN 10: 3659507032
ISBN 13: 9783659507038
Nuevo
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents a new augmentation method to eliminate the multicollinearity in datasets that contain several correlated predictor variables. The objective in mind is to reduce the estimation error of the regression coefficients. The main contribution of this work consists in offering a new alternative to eliminate multicollinearity in datasets by using small runs which are added in a sequential manner. The algorithm proposed will indicate the point in which the augmentations have sufficiently contributed to find the true regression model. The procedure is based on addition of new observations to the point in which an appropriate regression model can be constructed. The new information is obtained through designed experiments using the R3 algorithm as a guideline to perform the augmentations and the Ridge Trace and VIF statistic as verification tools that help to determine the point in which the correlations have been significantly reduced. The final result is a linear regression model that accurately represents the process under study. Nº de ref. del artículo: 9783659507038
Cantidad disponible: 1 disponibles
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A revision on Multicollinearity and Augmentation Methods
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LAP LAMBERT Academic Publishing, 2014
ISBN 10: 3659507032
ISBN 13: 9783659507038
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A Revision on Multicollinearity and Augmentation Methods
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ISBN 13: 9783659507038
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Condición: New. pp. 76. Nº de ref. del artículo: 26127631401
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A Revision on Multicollinearity and Augmentation Methods
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
ISBN 10: 3659507032
ISBN 13: 9783659507038
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A Revision on Multicollinearity and Augmentation Methods
Publicado por
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
ISBN 10: 3659507032
ISBN 13: 9783659507038
Nuevo
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Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Condición: New. PRINT ON DEMAND pp. 76. Nº de ref. del artículo: 18127631395
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A revision on Multicollinearity and Augmentation Methods
Publicado por
LAP LAMBERT Academic Publishing Jan 2014, 2014
ISBN 10: 3659507032
ISBN 13: 9783659507038
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Neuware -This book presents a new augmentation method to eliminate the multicollinearity in datasets that contain several correlated predictor variables. The objective in mind is to reduce the estimation error of the regression coefficients. The main contribution of this work consists in offering a new alternative to eliminate multicollinearity in datasets by using small runs which are added in a sequential manner. The algorithm proposed will indicate the point in which the augmentations have sufficiently contributed to find the true regression model. The procedure is based on addition of new observations to the point in which an appropriate regression model can be constructed. The new information is obtained through designed experiments using the R3 algorithm as a guideline to perform the augmentations and the Ridge Trace and VIF statistic as verification tools that help to determine the point in which the correlations have been significantly reduced. The final result is a linear regression model that accurately represents the process under study.Books on Demand GmbH, Überseering 33, 22297 Hamburg 76 pp. Englisch. Nº de ref. del artículo: 9783659507038
Cantidad disponible: 2 disponibles