A revision on Multicollinearity and Augmentation Methods | Multicollinearity and Augmentation Methods

Russell Pérez González (u. a.)

ISBN 10: 3659507032 / ISBN 13: 9783659507038

Published by LAP LAMBERT Academic Publishing, 2014

About this Item

A revision on Multicollinearity and Augmentation Methods | Multicollinearity and Augmentation Methods | Russell Pérez González (u. a.) | Taschenbuch | 76 S. | Englisch | 2014 | LAP LAMBERT Academic Publishing | EAN 9783659507038 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Seller Inventory # 105503646

Bibliographic Details

• Title: A revision on Multicollinearity and Augmentation Methods | Multicollinearity and Augmentation Methods
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2014
• Language: English
• ISBN 10: 3659507032
• ISBN 13: 9783659507038
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 5D millimeters 220 millimeters width by 150 millimeters height by 5 millimeters depth
• Weight: 131 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.

About the Author

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.