We consider high-dimensional generalized linear models with Lipschitz loss functions, and prove a nonasymptotic oracle inequality for the empirical risk minimizer with Lasso penalty. The penalty is ...

Second-order influence functions appear to be useful for computing these classification efficiencies. It turns out that, when using an appropriate robust estimator, the loss in classification ...

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, ...

Generalized Linear Models (GLMs) and Categorical Data Analysis (CDA) Course Topics Generally speaking, there are two types of outcomes (i.e. response) in statistical analysis: continuous and ...