High Dimensional Data Methods

With the rapid advancement of technology, data collection procedures have continuously improved. This has created a crucial need for statistical methods that can handle massive (and often noisy) data sets in many application areas. Center faculty have been at the forefront of developing theory and software that address key challenges when working in such high-dimensional settings. This broadly includes, but is not limited to dealing with missing data, finding scalable solutions for estimating model parameters, overcoming combinatorial issues when trying to identify nonlinear interactions, effectively modeling non-continuous outcomes (e.g. categorical data), and quantifying uncertainty with novel model validation/calibration techniques.

Stavroula Chrysanthopoulou

Lorin Crawford

Roberta DeVito

 

Fenghai Duan Zhijin Wu    
BACK TO THEORY AND METHODS

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