Estimating Structural Dimensionality using Contour Regression

Contour regression for estimating the central subspace is a method based on estimating contour directions of small variation in the response. These directions span the orthogonal complement of the central subspace and can be extracted according to two measures of variation in the response: simple and general contour regression (SCR and GCR). This paper investigates the estimation for structural dimensionality of the central space. We propose two kernel simple and general contour regression (KSCR and KGCR) to estimate structural dimensionality of the central space.