  We investigate the application of sufficient dimension reduction (SDR) to a
noiseless data set derived from a deterministic function of several variables.
In this context, SDR provides a framework for ridge recovery. In this second
part, we explore the numerical subtleties associated with using two inverse
regression methods---sliced inverse regression (SIR) and sliced average
variance estimation (SAVE)---for ridge recovery. This includes a detailed
numerical analysis of the eigenvalues of the resulting matrices and the
subspaces spanned by their columns. After this analysis, we demonstrate the
methods on several numerical test problems.
