Accurate and automatic prediction of cognitive assessment from multiple neuroimaging
biomarkers is crucial for early detection of Alzheimer's disease. The major challenges arise …

We propose a method for simultaneously estimating regression coefficients and clustering
response variables in a multivariate regression model, to increase prediction accuracy and …

In the process of sterilization and disinfection, chemical reagents will react with natural
organic matter and inorganic matter in water, and inevitably generate disinfection …

Multivariate regression (or multi-task learning) concerns the task of predicting the value of
multiple responses from a set of covariates. In this article, we propose a convex optimization …

Multivariate linear regressions are widely used to model the associations between multiple
related responses and a set of predictors. To infer such associations, researchers often test …

Multivariate responses are commonly encountered in many applications with high-
dimensional input variables. Feature screening has been shown to be a very useful data …

Multivariate regression models are widely used in various fields for fitting multiple
responses. In this paper, we proposed a sparse Laplacian shrinkage estimator for the high …

We develop a new method to fit the multivariate response linear regression model that
exploits a parametric link between the regression coefficient matrix and the error covariance …

We propose a new class of non‐convex penalties based on data depth functions for
multitask sparse penalized regression. These penalties quantify the relative position of rows …

Multivariate regression models are widely used in various fields such as biology and
finance. In this paper, we focus on two key challenges:(a) When should we favor a …