Analysis of Double Single Index Models.
Motivated from problems in canonical correlation analysis, reduced rank regression and sufficient dimension reduction, we introduce a double dimension reduction model where a single index of the multivariate response is linked to the multivariate covariate through a single index of these covariates, hence the name double single index model. Since nonlinear association between two sets of multivariate variables can be arbitrarily complex and even intractable in general, we aim at seeking a principal one-dimensional association structure where a response index is fully characterized by a single predictor index. The functional relation between the two single-indices is left unspecified, allowing flexible exploration of any potential nonlinear association. We argue that such double single index association is meaningful and easy to interpret, and the rest of the multi-dimensional dependence structure can be treated as nuisance in model estimation. We investigate the estimation and inference of both indices and the regression function, and derive the asymptotic properties of our procedure. We illustrate the numerical performance in finite samples and demonstrate the usefulness of the modeling and estimation procedure in a multi-covariate multi-response problem concerning concrete.
Department of Statistics, University of Connecticut, 215 Glenbrook Road U-4120, Storrs, Connecticut 06269, U.S.A.
Kun
K
Chen
Department of Statistics, University of South Carolina, 1523 Greene Street Columbia, SC 29208, U.S.A.
Yanyuan
Y
Ma
Canonical correlation analysis; Reduced rank regresion; Semiparametric efficiency; Single index models; Sufficient dimension reduction