Choosing Appropriate Regression Model in the Presence of Multicolinearity

Autor:	Maruf A. Raheem, Aramide Titilayo Gbolahan, Nse S. Udoh
Rok vydání:	2019
Předmět:	Variance inflation factor Mean squared error Multicollinearity Linear regression Statistics Principal component regression Regression analysis Simple linear regression Regression Mathematics
Zdroj:	Open Journal of Statistics. :159-168
ISSN:	2161-7198 2161-718X
DOI:	10.4236/ojs.2019.92012
Popis:	This work is geared towards detecting and solving the problem of multicolinearity in regression analysis. As such, Variance Inflation Factor (VIF) and the Condition Index (CI) were used as measures of such detection. Ridge Regression (RR) and the Principal Component Regression (PCR) were the two other approaches used in modeling apart from the conventional simple linear regression. For the purpose of comparing the two methods, simulated data were used. Our task is to ascertain the effectiveness of each of the methods based on their respective mean square errors. From the result, we found that Ridge Regression (RR) method is better than principal component regression when multicollinearity exists among the predictors.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e425f31b9990e80f88c3c07575af43be https://doi.org/10.4236/ojs.2019.92012 Zobrazit plný text záznamu