Multidimensional multiscale scanning in Exponential Families: Limit theory and statistical consequences
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In this paper we consider the problem of finding anomalies in a d-dimensional field of independent random variables {Y_i}_i ∈{1,...,n}^d, each distributed according to a one-dimensional natural exponential family F = {F_θ}_θ∈Θ. Given some baseline parameter θ_0 ∈Θ, the field is scanned using local likelihood ratio tests to detect from a (large) given system of regions R those regions R ⊂{1,...,n}^d with θ_i ≠θ_0 for some i ∈ R. We provide a unified methodology which controls the overall family wise error (FWER) to make a wrong detection at a given error rate. Fundamental to our method is a Gaussian approximation of the asymptotic distribution of the underlying multiscale scanning test statistic with explicit rate of convergence. From this, we obtain a weak limit theorem which can be seen as a generalized weak invariance principle to non identically distributed data and is of independent interest. Furthermore, we give an asymptotic expansion of the procedures power, which yields minimax optimality in case of Gaussian observations.
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