@article{BlanchardCarpentierGutzeit2018,
  author    = {Blanchard, Gilles and Carpentier, Alexandra and Gutzeit, Maurilio},
  title     = {Minimax Euclidean separation rates for testing convex hypotheses in R-d},
  series = {Electronic journal of statistics},
  volume    = {12},
  journal   = {Electronic journal of statistics},
  number    = {2},
  publisher = {Institute of Mathematical Statistics},
  address   = {Cleveland},
  issn      = {1935-7524},
  doi       = {10.1214/18-EJS1472},
  pages     = {3713 -- 3735},
  year      = {2018},
  abstract  = {We consider composite-composite testing problems for the expectation in the Gaussian sequence model where the null hypothesis corresponds to a closed convex subset C of R-d. We adopt a minimax point of view and our primary objective is to describe the smallest Euclidean distance between the null and alternative hypotheses such that there is a test with small total error probability. In particular, we focus on the dependence of this distance on the dimension d and variance 1/n giving rise to the minimax separation rate. In this paper we discuss lower and upper bounds on this rate for different smooth and non-smooth choices for C.},
  language  = {en}
}