Paper detail

Signed Support Recovery for Single Index Models in High-Dimensions

In this paper we study the support recovery problem for single index models $Y=f(\boldsymbol{X}^{\intercal} \boldsymbolβ,\varepsilon)$, where $f$ is an unknown link function, $\boldsymbol{X}\sim N_p(0,\mathbb{I}_{p})$ and $\boldsymbolβ$ is an $s$-sparse unit vector such that $\boldsymbolβ_{i}\in \{\pm\frac{1}{\sqrt{s}},0\}$. In particular, we look into the performance of two computationally inexpensive algorithms: (a) the diagonal thresholding sliced inverse regression (DT-SIR) introduced by Lin et al. (2015); and (b) a semi-definite programming (SDP) approach inspired by Amini & Wainwright (2008). When $s=O(p^{1-δ})$ for some $δ>0$, we demonstrate that both procedures can succeed in recovering the support of $\boldsymbolβ$ as long as the rescaled sample size $κ=\frac{n}{s\log(p-s)}$ is larger than a certain critical threshold. On the other hand, when $κ$ is smaller than a critical value, any algorithm fails to recover the support with probability at least $\frac{1}{2}$ asymptotically. In other words, we demonstrate that both DT-SIR and the SDP approach are optimal (up to a scalar) for recovering the support of $\boldsymbolβ$ in terms of sample size. We provide extensive simulations, as well as a real dataset application to help verify our theoretical observations.