Efficient Estimation of the Central Mean Subspace via Smoothed Gradient Outer Products

• URL: http://arxiv.org/abs/2312.15469v2
• Date: Fri, 13 Sep 2024 06:59:18 GMT
• Title: Efficient Estimation of the Central Mean Subspace via Smoothed Gradient Outer Products
• Authors: Gan Yuan, Mingyue Xu, Samory Kpotufe, Daniel Hsu,
• Abstract summary: We consider the problem of sufficient dimension reduction for multi-index models. We show that a fast parametric convergence rate of form $C_d cdot n-1/2$ is achievable.
• Score: 12.047053875716506
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider the problem of sufficient dimension reduction (SDR) for multi-index models. The estimators of the central mean subspace in prior works either have slow (non-parametric) convergence rates, or rely on stringent distributional conditions (e.g., the covariate distribution $P_{\mathbf{X}}$ being elliptical symmetric). In this paper, we show that a fast parametric convergence rate of form $C_d \cdot n^{-1/2}$ is achievable via estimating the \emph{expected smoothed gradient outer product}, for a general class of distribution $P_{\mathbf{X}}$ admitting Gaussian or heavier distributions. When the link function is a polynomial with a degree of at most $r$ and $P_{\mathbf{X}}$ is the standard Gaussian, we show that the prefactor depends on the ambient dimension $d$ as $C_d \propto d^r$.

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