  We consider estimating the parametric components of semi-parametric multiple
index models in a high-dimensional and non-Gaussian setting. Such models form a
rich class of non-linear models with applications to signal processing, machine
learning and statistics. Our estimators leverage the score function based first
and second-order Stein's identities and do not require the covariates to
satisfy Gaussian or elliptical symmetry assumptions common in the literature.
Moreover, to handle score functions and responses that are heavy-tailed, our
estimators are constructed via carefully thresholding their empirical
counterparts. We show that our estimator achieves near-optimal statistical rate
of convergence in several settings. We supplement our theoretical results via
simulation experiments that confirm the theory.
