Seeking relevant information from a statistical model

Abstract

We herein introduce a general variable selection procedure, which can be applied to several parametric multivariate problems, including principal components and regression, among others. The aim is to allow the identification of a small subset of the original variables that can ‘better explain’ the model through nonparametric relationships. The method typically yields some noisy uninformative variables and some variables that are strongly related because of their general dependence and our aim is to help understand the underlying structures in a given data–set. The asymptotic behaviour of the proposed method is considered and some real and simulated data–sets are analysed as examples.