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 …

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

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

Deletion/Substitution/Addition algorithm. The optimal learner for prediction will vary depending on the underlying data …-generating distribution. In this article we introduce the "super learner", a prediction algorithm that applies any set of candidate learners …

of right-censoring for the prediction of survival. Furthermore, we introduce how to incorporate bagging into the …

of right-censoring for the prediction of survival. Furthermore, we introduce how to incorporate bagging into the …

Deletion/Substitution/Addition algorithm. The optimal learner for prediction will vary depending on the underlying data …-generating distribution. In this article we introduce the "super learner", a prediction algorithm that applies any set of candidate learners …

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 …