Lin, QianXinran, LiHuang, DongmingLiu, Jun2021-04-132021-02-03Lin, Qian, Xinran Li, Dongming Huang, and Jun S Liu. "On the Optimality of Sliced Inverse Regression in High Dimensions." The Annals of Statistics 49, no. 1 (2021): 1.0090-5364https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37367262The central subspace of a pair of random variables (y, x) ∈ R^{p+1} is the minimal subspace S such that y ⊥ x|P_Sx. In this paper, we consider the minimax rate of estimating the central space under the multiple index model y = f (β1x,β2x, . . . ,βdx, e) with at most s active predictors, where x ∼ N(0,Sigma) for some class of Sigma. We first introduce a large class of models depending on the smallest nonzero eigenvalue λ of var(E[x|y]), over which we show that an aggregated estimator based on the SIR procedure converges at rate d ∧ ((sd + s log(ep/s))/(nλ)). We then show that this rate is optimal in two scenarios, the single index models and the multiple index models with fixed central dimension d and fixed λ. By assuming a technical conjecture, we can show that this rate is also optimal for multiple index models with bounded dimension of the central space.en-USStatistics, Probability and UncertaintyStatistics and ProbabilityOptimal ratessemidefinite positive programmingsliced inverse regressionsufficient dimension reductionJournal Article2021-04-1310.1214/19-aos1813