| Robust high-dimensional data analysis using a weight shrinkage rule |
2016 |
2821 |
In high-dimensional settings, a penalized least squares approach may lose its efficiency in both estimation and variable selection due to the existence of either outliers or heteroscedasticity. In this thesis, we propose a novel approach to perform r... |