Abstract
In this paper we present a multiscale approach for change point detection. The algorithm estimates likelihood-ratio (LR) test in several scrolling windows simultaneously. This makes the method adaptive to structural breaks of different scales. Critical values are calibrated in a data-driven way using multiplier bootstrap, which estimates nonasymptotic distribution of the test statistics.
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