Gaussian Mean Testing under Truncation

We consider the task of Gaussian mean testing, that is, of testing whether a high-dimensional vector perturbed by white noise has large magnitude, or is the zero vector. This question, originating from the signal processing community, has recently seen a surge of interest from the machine learning and theoretical computer science community, and is by now fairly well understood. What is much less understood, and the focus of our work, is how to perform this task under *truncation*: that is, when the observations (i.i.d. samples from the underlying high-dimensional Gaussian) are only observed when they fall in an given subset of the domain $\mathbb{R}^d$. This truncation model, previously studied in the context of *learning* (instead of *testing*) the mean vector, has a range of applications, in particular in Economics and Social Sciences. As our work shows, sample truncations affect the complexity of the testing task in a rather subtle and surprising way.