Adaptive wavelet multivariate regression with errors in variables
  In the multidimensional setting, we consider the errors-in-variables model. We aim at estimating the unknown nonparametric multivariate regression function with errors in the covariates. We devise an adaptive estimator based on projection kernels on wavelets and a deconvolution operator. We propose an automatic and fully data driven procedure to select the wavelet level resolution. We obtain an oracle inequality and optimal rates of convergence over anisotropic H{\"o}lder classes. Our theoretical results are illustrated by some simulations. 
Micha\"el Chichignoud, Van Ha Hoang (LPP), Thanh Mai Pham Ngoc   (LM-Orsay), Vincent Rivoirard (CEREMADE)
math.ST stat.TH