Group model selection is the problem of determining a small subset of groups of
predictors responsible for majority of the variation in a response variable.
This paper focuses on group model selection in high-dimensional linear models,
in which the number of predictors far exceeds the number of samples of the
response variable. Existing works on high-dimensional group model selection
either require the number of samples of the response variable to be
significantly larger than the total number of predictors contributing to
the response or impose restrictive statistical priors on the predictors and/or
nonzero regression coeffecients. This paper provides comprehensive
understanding of a low complexity approach to group model selection that avoids
some of these limitations.