A unified approach to self-normalized block sampling

STOCHASTIC PROCESSES AND THEIR APPLICATIONS

卷:126

Article

Bai, Shuyang¹  Taqqu, Murad S.¹  Zhang, Ting¹ 

[1] Dept Math & Stat, 111 Cummington Mall, Boston, MA 02215 USA

关键词: Time series;    Subsampling;    Block sampling;    Sampling window;    Self-normalization;    Heavy tails;    Long-range dependence;    Long memory;   

DOI  :  10.1016/j.spa.2016.02.007

来源: Elsevier

【 摘 要 】

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. (C) 2016 Elsevier B.V. All rights reserved.

【 授权许可】

Free