Reduced rank approximation of matrices has hitherto been possible only by unweighted least squares. This paper presents iterative techniques for obtaining such approximations when weights are ...

We consider polynomial approximation on the unit sphere S² = {(x, y, z) Є R³ : x² + y² + z² = 1} by a class of regularized discrete least squares methods with novel choices for the regularization ...

Learn how the Least Squares Criterion determines the line of best fit for data analysis, enhancing predictive accuracy in finance, economics, and investing.