Abstract
In solving the regression equations, collinearity in the design matrix can result in parameter estimates which are inaccurate. The use of orthogonal matrix transformations such as the singular-value decomposition can reduce the effect of collinearity. Also, estimates of the regression coefficients can sometimes be improved through the process known as iterative refinement. The application of iterative refinement to the singular-value decomposition solution of the regression equations is described. Tests show that iterative refinement using the singular-value decomposition can improve regression coefficient estimates, in cases where the design matrix is highly collinear.
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