Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10527306 | Stochastic Processes and their Applications | 2016 | 29 Pages |
Abstract
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 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.
Keywords
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Physical Sciences and Engineering
Mathematics
Mathematics (General)
Authors
Shuyang Bai, Murad S. Taqqu, Ting Zhang,