methodology, which provides a road map for estimation and performance assessment. Given a parameter of interest which can be …-validation for estimator selection and minimizing over subsets of basis functions the empirical risk of the subset-specific estimator …/substitution/addition algorithm for minimizing over subsets of variables (e.g., basis functions) the empirical risk of subset-specific estimators of …

van der Laan and Dudoit (2003) provide a road map for estimation and performance assessment where a parameter of … interest is defined as the risk minimizer for a suitable loss function and candidate estimators are generated using a loss … minimizing, over subsets of variables (e.g., basis functions), the empirical risk of subset-specific estimators of the parameter …

van der Laan and Dudoit (2003) provide a road map for estimation and performance assessment where a parameter of … interest is defined as the risk minimizer for a suitable loss function and candidate estimators are generated using a loss … minimizing, over subsets of variables (e.g., basis functions), the empirical risk of subset-specific estimators of the parameter …

negative log-density loss function in density estimation. Minimizing the empirical risk (i.e., the empirical mean of the loss …-validated epsilon-net estimation methodology that covers a broad class of estimation problems, including multivariate outcome prediction … interest (i.e., the risk minimizer for the true data generating distribution). In this article, we propose a cross …

Use of microarray technology often leads to high-dimensional and low-sample size (HDLSS) data settings. A variety of … of microarray data obtained from a set of patients with diffuse large B-cell lymphoma where time to survival is of …