Some extensions of multivariate sliced inverse regression
Multivariate sliced inverse regression ( SIR) is a method for achieving dimension reduction in regression problems when the outcome variable y and the regressor x are both assumed to be multidimensional. In this paper, we extend the existing approaches, based on the usual SIRI which only uses the inverse regression curve, to methods using properties of the inverse conditional variance. Contrary to the existing ones, these new methods are not blind for symmetric dependencies and rely on the SIRII or SIR alpha. We also propose their corresponding pooled slicing versions. We illustrate the usefulness of these approaches on simulation studies.
sliced inverse regression (sir) pooled slicing complete slicing marginal slicing pooled marginal slicing alternating sir semi-parametric regression model