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What is Learnable in Valiant's Theory of the Learnable? 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Non asymptotic minimax rates of testing in signal detection with heterogeneous variances
2009-12-12 · via math.ST updates on arXiv.org

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 $(θ_j)_{j\in J}$ and variance $(σ_j)_{j\in J}$. 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 $l_{\infty}$ norms, without assumption on $(σ_j)_{j\in J}$ and on several functions spaces. Our point of view is completely non-asymptotic.

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