  In the statistical inference for long range dependent time series the shape
of the limit distribution typically depends on unknown parameters. Therefore,
we propose to use subsampling. We show the validity of subsampling for general
statistics and long range dependent subordinated Gaussian processes which
satisfy mild regularity conditions. We apply our method to a self-normalized
change-point test statistic so that we can test for structural breaks in long
range dependent time series without having to estimate any nuisance parameter.
The finite sample properties are investigated in a simulation study. We analyze
three data sets and compare our results to the conclusions of other authors.
