: Advancing efficient design for regularized linear models, ensuring that data collection is optimized for specific analytical goals. 3. Critical Applications
: Used for skewed, truncated, or contaminated data with outliers. Advances and Innovations in Statistics and Data...
: Incorporating statistical methods like word embedding clustering to rank comments and analyze text-based feedback. : Advancing efficient design for regularized linear models,
: Innovating techniques for feature screening and variable selection in datasets where the number of variables far exceeds the number of observations. Advances and Innovations in Statistics and Data...