Asymptotics for sliced average variance estimation

In this paper, we systematically study the consistency of sliced average variance estimation (SAVE). The findings reveal that when the response is continuous, the asymptotic behavior of SAVE is rather different from that of sliced inverse regression (SIR). SIR can achieve √ n consistency even when each slice contains only two data points. However, SAVE cannot be √ n consistent and it even turns out to be not consistent when each slice contains a fixed number of data points that do not depend on n, where n is the sample size. These results theoretically confirm the notion that SAVE is more sensitive to the number of slices than SIR. Taking this into account, a bias correction is recommended in order to allow SAVE to be √ n consistent. In contrast, when the response is discrete and takes finite values, √ n consistency can be achieved. Therefore, an approximation through discretization, which is commonly used in practice, is studied. A simulation study is carried out for the purposes of illustration. 1. Introduction. Dimension reduction has become one of the most important issues in regression analysis because of its importance in dealing with problems with high-dimensional data. Let Y and x = (x 1 ,. .. , x p) T be the response and p-dimensional covariate, respectively. In the literature, when Y depends on x = (x 1 ,. .. , x p) T through a few linear combinations B T x of x, where B = (β 1 ,. .. , β k), there are several proposed methods for estimating the projection directions B/space that is spanned by B, such as projection pursuit regression (PPR) [11], the alternating conditional expectation (ACE) method [1], principal Hessian directions (pHd) [17], minimum average variance estimation (MAVE) [23], iterated pHd [7] and profile least-squares