The performance of latent root-M based regression. - Midi, Habshah; Lau, Ung Hua (Science Publications, 2009)
Problem statement: In the presence of multicollinearity, the estimation of parameters in multiple linear regression models by means of Ordinary Least Squares (OLS) is known to suffer severe distortion. An alternative approach was to use the modified OLS which was based on the latent roots and latent vectors of the correlation matrix of the independent and dependent variables. This procedure is called the Latent Root Regression (LRR) which serves the purpose to improve the stability of the estimates for data plagued by multicollinearity. However, there was evidence that the LRR estimators were easily affected by a few atypical observations that we call outliers. It is now evident that the robust method alone cannot rectify the combined problems of multicollinearity and outliers.