%0 Journal Article
%T A unified approach to self-normalized block sampling
%A Shuyang Bai
%A Murad S. Taqqu
%A Ting Zhang
%J Statistics
%D 2015
%I arXiv
%X The inference procedure for the mean of a stationary time series is usually quite different under various model assumptions because the partial sum process behaves differently depending on whether the time series is short or long-range dependent, or whether it has a light or heavy-tailed marginal distribution. In the current paper, we develop an asymptotic theory for the self-normalized block sampling, and prove that the corresponding block sampling method can provide a unified inference approach for the aforementioned different situations in the sense that it does not require the {\em a priori} estimation of auxiliary parameters. Monte Carlo simulations are presented to illustrate its finite-sample performance. The R function implementing the method is available from the authors.
%U http://arxiv.org/abs/1512.00820v1