A Sparse Regression Method for Group-Wise Feature Selection with False Discovery Rate Control.

ABSTRACT: The method of Sorted L-One Penalized Estimation, or SLOPE, is a sparse regression method recently introduced by Bogdan et. al. [1] . It can be used to identify significant predictor variables in a linear model that may have more unknown parameters than observations. When the correlations between predictor variables are small, the SLOPE method is shown to successfully control the false discovery rate (the expected proportion of the irrelevant among all selected predictors) at a user specified level. However, the requirement for nearly uncorrelated predictors is too restrictive for genomic data, as demonstrated in our recent study [2] by an application of SLOPE to realistic simulated DNA sequence data. A possible solution is to divide the predictor variables into nearly uncorrelated groups,

SUBMITTER: Gossmann A 

PROVIDER: S-EPMC6326365 | biostudies-literature | 2018 Jul-Aug

REPOSITORIES: biostudies-literature