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ABSTRACT. 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. Because 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 multidimensional
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 modelling and estimation procedure in a multi-covariate multi-response
problem concerning concrete.
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