Clément Marteau

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

Minimax fast rates for discriminant analysis with errors in variables

(Y_j)_{j\in J}

Intensity estimation of non-homogeneous Poisson processes from shifted trajectories

of independent Gaussian random variables, having mean

General regularization schemes for signal detection in inverse problems

(\theta_j)_{j\in J}

Signal detection for inverse problems in a multidimensional framework

and variance

The Stein hull

(\sigma_j)_{j\in J}

Sharp template estimation in a shifted curves model

. The set

Minimax signal detection under weak noise assumptions

J

Minimax Goodness-of-Fit Testing in Ill-Posed Inverse Problems with Partially Unknown Operators

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

Goodness-of-fit testing strategies from indirect observations

l_2