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 ...
We establish adaptive results for trend filtering: least squares estimation with a penalty on the total variation of (k − 1)th order differences. Our approach is based on combining a general oracle ...
Learn how the Least Squares Criterion determines the line of best fit for data analysis, enhancing predictive accuracy in finance, economics, and investing.
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