Gao, Xiaoli


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Robust high-dimensional data analysis using a weight shrinkage rule 2016 1484 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...