 For example, sliced inverse regression (SIR) and sliced average variance estimation (SAVE) were introduced in the 1990s and continue to be widely used.Although SIR was originally designed to estimate an "effective dimension reducing subspace", it is now understood that it estimates only the central subspace, which is generally different.More recent methods for dimension reduction include likelihood-based sufficient dimension reduction, estimating the central subspace based on the inverse third moment (or "k"th moment), estimating the central solution space, graphical regression,envelope model, and the principal support vector machine.