Estimating the structural dimension of regressions via parametric inverse regression

Efstathia Bura and R. Dennis Cook

Journal of the Royal Statistical Society Series B, 2001, vol. 63, issue 2, 393-410

Abstract: A new estimation method for the dimension of a regression at the outset of an analysis is proposed. A linear subspace spanned by projections of the regressor vector X, which contains part or all of the modelling information for the regression of a vector Y on X, and its dimension are estimated via the means of parametric inverse regression. Smooth parametric curves are fitted to the p inverse regressions via a multivariate linear model. No restrictions are placed on the distribution of the regressors. The estimate of the dimension of the regression is based on optimal estimation procedures. A simulation study shows the method to be more powerful than sliced inverse regression in some situations.

Date: 2001
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