  In this paper, we use the stochastic approximation method to estimate Sliced
Average Variance Estimation (SAVE). This method is known for its efficiency in
recursive estimation. Stochastic approximation is particularly effective for
constructing recursive estimators and has been widely used in density
estimation, regression, and semi-parametric models. We demonstrate that the
resulting estimator is asymptotically normal and root n consistent. Through
simulations conducted in the laboratory and applied to real data, we show that
it is faster than the kernel method previously proposed.
