Subsampling for General Statistics under Long Range Dependence

In the statistical inference for long range dependent time series, the shape of the limit distribution typically dependents 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 and investigate the finite sample properties in a simulation study.