In this article, we propose a new canonical correlation method based on information theory. This method examines potential nonlinear relationships between px1 vector Y-set and qx1 vector X-set. It finds canonical coefficient vectors a and b by maximizing a more general measure, the mutual...

In this paper we propose a dimension reduction method for estimating the directions in a multiple-index regression based on information extraction. This extends the recent work of Yin and Cook [X. Yin, R.D. Cook, Direction estimation in single-index regression, Biometrika 92 (2005) 371-384] who...

We introduce a new method for estimating the direction in single-index models via distance covariance. Our method keeps model-free advantage as a dimension reduction approach. In addition, no smoothing technique is needed, which enables our method to work efficiently when many predictors are...