Logo
DeutschClear Cookie - decide language by browser settings
Petry, Sebastian and Tutz, Gerhard (2011): The OSCAR for Generalized Linear Models. Department of Statistics: Technical Reports, No.112
[img]
Preview

PDF

745kB

Abstract

The Octagonal Selection and Clustering Algorithm in Regression (OSCAR) proposed by Bondell and Reich (2008) has the attractive feature that highly correlated predictors can obtain exactly the same coecient yielding clustering of predictors. Estimation methods are available for linear regression models. It is shown how the OSCAR penalty can be used within the framework of generalized linear models. An algorithm that solves the corresponding maximization problem is given. The estimation method is investigated in a simulation study and the usefulness is demonstrated by an example from water engineering.