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The problem of testing for the presence of epidemic changes in random fieldsis investigated. In order to be able to deal with general changes in themarginal distribution, a Cram\'er-von Mises type test is introduced which isbased on Hilbert space theory. A functional central limit theorem for$\rho$-mixing Hilbert space valued random fields is proven. In order to avoidthe estimation of the long-run variance and obtain critical values, Shao'sdependent wild bootstrap method is adapted to this context. For this, a jointfunctional central limit theorem for the original and the bootstrap sample isshown. Finally, the theoretic results are supplemented by a short simulationstudy.

Change-Point Detection and Bootstrap for Hilbert Space Valued Random Fields