  We propose a new approach for sparse regression and marginal testing, for
data with correlated features. Our procedure first clusters the features, and
then chooses as the cluster prototype the most informative feature in that
cluster. Then we apply either sparse regression (lasso) or marginal
significance testing to these prototypes. While this kind of strategy is not
entirely new, a key feature of our proposal is its use of the post-selection
inference theory of Taylor et al. (2014) and Lee et al. (2014) to compute exact
p-values and confidence intervals that properly account for the selection of
prototypes.
  We also apply the recent "knockoff" idea of Barber and Cand\`es to provide
exact finite sample control of the FDR of our regression procedure. We
illustrate our proposals on both real and simulated data.
