Abstract
This paper considers the problem of statistical inference based on the one-sample sign statistic for strictly stationary random processes which exhibit long-range dependence. Under Gaussian subordination, the limiting distribution of the sign statistic may be non-Gaussian and depends on unknown parameters. We examine how asymptotically valid inference may be carried out using subsampling. The small-sample performance of the method is also investigated by means of Monte Carlo experiments.
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