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 …

estimators in the prediction of a univariate outcome by minimizing an empirical risk, and it uses cross-validation to select fine …

a prediction of viral replication capacity based on an entire mutant/non-mutant sequence profile. This is a loss … estimators in the prediction of a univariate outcome by minimizing an empirical risk. These methods are two separate techniques …

and an outcome. For example, though prediction/machine learning is, in principle, concerned with learning the optimal … for each input variable. The approach in prediction has been to learn the unknown optimal predictor from the data and … this methodology in the context of prediction, and obtain in this manner double robust locally optimal estimators of …

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