False discoveries occur early on the lasso path
In regression settings where explanatory variables have very low correlations and there are relatively few effects, each of large magnitude, we expect the Lasso to find the important variables with few errors, if any. This paper shows that in a regime of linear sparsity-meaning that the fraction of variables with a nonvanishing effect tends to a constant, however small-this cannot really be the ca