A Revision on Multicollinearity and Augmentation Methods - Tapa blanda

Perez Gonzalez Russell; Rios Lira Armando Javier; Arias Nava Elias Heriberto

9783659507038: A Revision on Multicollinearity and Augmentation Methods

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ISBN 10: 3659507032 ISBN 13: 9783659507038

Editorial: LAP Lambert Academic Publishing, 2014

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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.

Biograf�a del autor

M.I.E. Russell P�rez Gonz�lez received his I.E. from Instituto de Tuxtla Guti�rrez, 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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EditorialLAP Lambert Academic Publishing
A�o de publicaci�n2014
ISBN 10 3659507032
ISBN 13 9783659507038
Encuadernaci�nTapa blanda
IdiomaIngl�s
N�mero de p�ginas76

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A revision on Multicollinearity and Augmentation Methods

Russell P�rez Gonz�lez

Publicado por LAP LAMBERT Academic Publishing Jan 2014, 2014

ISBN 10: 3659507032 ISBN 13: 9783659507038

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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania

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Taschenbuch. Condici�n: 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. N� de ref. del art�culo: 9783659507038

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A Revision on Multicollinearity and Augmentation Methods

P�rez Gonz�lez, Russell; R�os Lira, Armando Javier; Arias Nava, El�as Heriberto

Publicado por VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014

ISBN 10: 3659507032 ISBN 13: 9783659507038

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Condici�n: New. Print on Demand pp. 76 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam. N� de ref. del art�culo: 132923382

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A revision on Multicollinearity and Augmentation Methods : Multicollinearity and Augmentation Methods

Russell P�rez Gonz�lez

Publicado por LAP LAMBERT Academic Publishing, 2014

ISBN 10: 3659507032 ISBN 13: 9783659507038

Nuevo Taschenbuch

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Librería: AHA-BUCH GmbH, Einbeck, Alemania

Calificaci�n del vendedor: 5 de 5 estrellas

Taschenbuch. Condici�n: 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. N� de ref. del art�culo: 9783659507038

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