Non asymptotic minimax rates of testing in signal detection with heterogeneous variances

The aim of this paper is to establish non-asymptotic minimax rates of testing for goodness-of-fit hypotheses in a heteroscedastic setting. More precisely, we deal with sequences $(Y_j{)}_{j\in J}$ of independent Gaussian random variables, having mean $({\theta }_j{)}_{j\in J}$ and variance $({\sigma }_j{)}_{j\in J}$. The set $J$ will be either finite or countable. In particular, such a model covers the inverse problem setting where few results in test theory have been obtained. The rates of testing are obtained with respect to $l_2$ and $l_{\mathrm{\infty }}$ norms, without assumption on $({\sigma }_j{)}_{j\in J}$ and on several functions spaces. Our point of view is completely non-asymptotic.