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

Subsampling for General Statistics under Long Range Dependence with application to change point analysis