Estimating Outliers Using the Iterative Method in Partial Least Squares Regression Analysis for Linear Models.

Outliers affect the accuracy of the estimated parameters of the partial least squares regression model and give unacceptably large residual values. Traditional robust methods (used in ordinary least squares) cannot be used to treat outliers in estimating partial least squares regression model, due to the number of independent variables greater than the sample size, therefore, it was proposed to use an iterative method to treat outliers and estimation of partial least squares regression model parameters. The iterative method relies on identifying outliers and then estimating them using the initial estimated values and the residual and determining the optimal value that gives the least sum of squares error for the partial least square regression model. To illustrate the proposed method, simulated and real data were used based on a program MATLAB designed for this purpose. The proposed method provided accurate results for the partial squares regression model parameters depending on MSE criteria and addressed the problem of outliers.