Subsampling for general statistics under long range dependence

Abstract

In the statistical inference for long range dependent time series, the shape of the limit distribution typically dependents on unknown param- eters. 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.