An Efficient Dual ADMM for Huber Regression with Fused Lasso Penalty

Abstract

The ordinary least squares estimate in linear regression is sensitive to the influence of errors with large variance, which reduces its robustness, especially when dealing with heavy-tailed errors or outliers frequently encountered in real-world scenarios. To address this issue and accommodate the sparsity of coefficients along with their sequential disparities, we combine the adaptive robust Huber loss function with a fused lasso penalty. This combination yields a robust estimator capable of simultaneously achieving estimation and variable selection. Furthermore, we utilize an efficient alternating direction method of multipliers to solve this regression model from a dual perspective. The effectiveness and efficiency of our proposed approach is demonstrated through numerical experiments carried out on both simulated and real datasets.