GUEST - Graphical Models in Ultrahigh-Dimensional and Error-Prone Data
via Boosting Algorithm
We consider the ultrahigh-dimensional and error-prone
data. Our goal aims to estimate the precision matrix and
identify the graphical structure of the random variables with
measurement error corrected. We further adopt the estimated
precision matrix to the linear discriminant function to do
classification for multi-label classes.
