The critical threshold level on Kendall's tau statistic concerning   minimax estimation of sparse correlation matrices
  In a sparse high-dimensional elliptical model we consider a hard threshold estimator for the correlation matrix based on Kendall's tau with threshold level $\alpha(\frac{\log p}{n})^{1/2}$. Parameters $\alpha$ are identified such that the threshold estimator achieves the minimax rate under the squared Frobenius norm and the squared spectral norm. This allows a reasonable calibration of the estimator without any quantitative information about the tails of the underlying distribution. For Gaussian observations we even establish a critical threshold constant $\alpha^\ast$ under the squared Frobenius norm, i.e. the proposed estimator attains the minimax rate for $\alpha>\alpha^\ast$ but in general not for $\alpha<\alpha^\ast$. To the best of the author's knowledge this is the first work concerning critical threshold constants. The main ingredient to provide the critical threshold level is a sharp large deviation expansion for Kendall's tau sample correlation evolved from an asymptotic expansion of the number of permutations with a certain number of inversions.   The investigation of this paper also covers further statistical problems like the estimation of the latent correlation matrix in the transelliptical and nonparanormal family. 
Kamil Jurczak
math.ST stat.TH