This paper provides an alternative approach to penalized regression for model selection in the context of high-dimensional linear regressions where the number of covariates is large, often much larger ...

We propose a multivariate sparse group lasso variable selection and estimation method for data with highdimensional predictors as well as high-dimensional response variables. The method is carried out ...

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...