Shrinkage and model selection with correlated variables via weighted fusion

In this paper, we propose the weighted fusion, a new penalized regression and variable selection method for data with correlated variables. The weighted fusion can potentially incorporate information redundancy among correlated variables for estimation and variable selection. Weighted fusion is also useful when the number of predictors pp is larger than the number of observations nn. It allows the selection of more than nn variables in a motivated way. Real data and simulation examples show that weighted fusion can improve variable selection and prediction accuracy.