Interaction Screening for Ultra-High Dimensional Data.

ABSTRACT: In ultra-high dimensional data analysis, it is extremely challenging to identify important interaction effects, and a top concern in practice is computational feasibility. For a data set with n observations and p predictors, the augmented design matrix including all linear and order-2 terms is of size n × (p² + 3p)/2. When p is large, say more than tens of hundreds, the number of interactions is enormous and beyond the capacity of standard machines and software tools for storage and analysis. In theory, the interaction selection consistency is hard to achieve in high dimensional settings. Interaction effects have heavier tails and more complex covariance structures than main effects in a random design, making theoretical analysis difficu

SUBMITTER: Hao N 

PROVIDER: S-EPMC4224119 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature