  In Change point detection task Likelihood Ratio Test (LRT) is sequentially
applied in a sliding window procedure. Its high values indicate changes of
parametric distribution in the data sequence. Correspondingly LRT values
require predefined bound for their maximum. The maximum value has unknown
distribution and may be calibrated with multiplier bootstrap. Bootstrap
procedure convolves independent components of the Likelihood function with
random weights, that enables to estimate empirically LRT distribution. For this
empirical distribution of the LRT we show convergence rates to the real maximum
value distribution.
