S2IR: Standardized Sliced Inverse Regression

Li Kevin (1)

1 BJTU - Beijing Jiaotong University

In this paper we consider a semiparametric regression model involving a $p$-dimensional explanatory variable ${\mathbf{x}}$ and including a dimension reduction of ${\mathbf{x}}$ via an index $B'{\mathbf{x}}$. In this model, the main goal is to estimate $B$ and to predict the real response variable $Y$ conditionally to ${\mathbf{x}}$. A standard approach is based on sliced inverse regression (SIR). We propose a new version of this method: the standardized sliced inverse regression.

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Li Kevin. S2IR: Standardized Sliced Inverse Regression. 2013. ⟨hal-00833686⟩