Group Logistic Regression Models with lp,q Regularization

Logistic Regression with Group `1 vs. Elastic Net Regularization

Shrinking Targets for Regularization of Logistic Regression

Multiclass Bounded Logistic Regression – Efficient Regularization with Interior Point Method

Logistic regression has been widely used in classification tasks for many years. Its optimization in case of linear separable data has received extensive study due to the problem of a monoton likelihood. This paper presents a new approach, called bounded logistic regression (BLR), by solving the logistic regression as a convex optimization problem with constraints. The paper tests the accuracy ...

Nonconvex Sparse Logistic Regression with Weakly Convex Regularization

In this work we propose to fit a sparse logistic regression model by a weakly convex regularized nonconvex optimization problem. The idea is based on the finding that a weakly convex function as an approximation of the `0 pseudo norm is able to better induce sparsity than the commonly used `1 norm. For a class of weakly convex sparsity inducing functions, we prove the nonconvexity of the corres...

Dealing with Separation in Logistic Regression Models

When facing small numbers of observations or rare events, political scientists often encounter separation, in which explanatory variables perfectly predict binary events or non-events. In this situation, maximum likelihood provides implausible estimates and the researcher might want incorporate some form of prior information into the model. The most sophisticated research uses Jeffreys’ invaria...