squared return prediction errors gives an adequate approximation of the unobserved realised conditional variance for both the …

and compare state-of-the-art statistical learning techniques for the prediction of shrimp harvest (in pounds) for a little … allowed, Linear Regression with best subset variable selection and SVM with linear Kernel gave the lowest prediction error …

with a new class of loss-based cross-validated algorithms in prediction of univariate and multivariate outcomes …

separately in the statistical literature, including multivariate outcome prediction and density estimation based on either … in genomic data analysis: the prediction of biological and clinical outcomes (possibly censored) using microarray gene …

-validated epsilon-net estimation methodology that covers a broad class of estimation problems, including multivariate outcome prediction …

When trying to learn a model for the prediction of an outcome given a set of covariates, a statistician has many … learners. Motivated by this use of cross validation, we propose a new prediction method for creating a weighted combination of … in prediction which uses V-fold cross-validation to select weights to combine an initial set of candidate learners. In …

of interest. This algorithm provides us with a new class of loss-based cross-validated algorithms in prediction of …