  The development and use of dimension reduction methods is prevalent in modern
statistical literature. This paper reviews a class of dimension reduction
techniques which aim to simultaneously select relevant predictors and find
clusters within them which share a common effect on the response. Such methods
have been shown to have superior performance relative to OLS estimates and the
lasso [Tibshirani, 1996] especially when multicollinearity in the predictors is
present. Their applications, which include genetics, epidemiology, and fMRI
studies, are also discussed.
