Filzmoser, P. and Groux, C. (2002) A Projection Algorithm for Regression with Collinearity. In: Jajuga, K., Sokolowski, A. and Bock, H.H., Eds., Classification, Clustering, and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization, Springer, Berlin, Heidelberg, 227-234.

has been cited by the following article:

• TITLE: Choosing Appropriate Regression Model in the Presence of Multicolinearity

  AUTHORS: Maruf A. Raheem, Nse S. Udoh, Aramide T. Gbolahan

  KEYWORDS: Multicollinearity, Adequacy, Regression coefficients, Variance Inflation Factor (VIF), Mean Square Error

  JOURNAL NAME: Open Journal of Statistics, Vol.9 No.2, April 1, 2019

  ABSTRACT: 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.