Title: Sparse regression for large data sets with outliers
Authors: Christophe Croux - Edhec Business School (France) [presenting]
Ines Wilms - Maastricht University (Netherlands)
Lea Bottmer - Stanford (United States)
Abstract: The linear regression model remains an important workhorse for data scientists. However, many data sets contain many more predictors than observations. Besides, outliers, or anomalies, frequently occur. An algorithm is proposed for regression analysis that addresses these features typical for big data sets. The resulting regression coefficients are sparse, meaning that many of them are set to zero, hereby selecting the most relevant predictors. A distinct feature of the method is its robustness with respect to outliers in the cells of the data matrix. The excellent performance of this robust variable selection and prediction method is shown in a simulation study. A real data application on car fuel consumption demonstrates its usefulness.