: Innovating techniques for feature screening and variable selection in datasets where the number of variables far exceeds the number of observations.
: Using geometric interpretations of distance for learning finite Gaussian mixtures, which provides robustness against model mis-specifications.
: Handling incomplete functional observations.
: Used for skewed, truncated, or contaminated data with outliers.
: Innovating techniques for feature screening and variable selection in datasets where the number of variables far exceeds the number of observations.
: Using geometric interpretations of distance for learning finite Gaussian mixtures, which provides robustness against model mis-specifications. Advances and Innovations in Statistics and Data...
: Handling incomplete functional observations. : Innovating techniques for feature screening and variable
: Used for skewed, truncated, or contaminated data with outliers. or contaminated data with outliers.