Gao, Xiaoli

uncg

There are 2 item/s.

TitleDateViewsBrief Description
Robust high-dimensional data analysis using a weight shrinkage rule 2016 2189 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...
Robust penalized regression for complex high-dimensional data 2020 401 Robust high-dimensional data analysis has become an important and challenging task in complex Big Data analysis due to the high-dimensionality and data contamination. One of the most popular procedures is the robust penalized regression. In this diss...