Lea Kaufmann

Fusion Consistency in High-Dimensional Logistic Regression with Factors

In a variety of applications, high-dimensional problems in a regression-type framework appear, for which penalized regression is known to be an attractive tool. In the presence of categorical explanatory variables, i.e. factors, it is desirable to apply a penalty function performing factor selection as well as leve...