ON THE RANGE OF VALIDITY OF THE AUTOREGRESSIVE SIEVE BOOTSTRAP

• 成果类型: Article
• 署名作者: Kreiss, Jens-Peter; Paparoditis, Efstathios; Politis, Dimitris N.
• 署名单位: Braunschweig University of Technology; University of Cyprus; University of California System; University of California San Diego
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/11-AOS900
• 发表日期: 2011
• 页码: 2103-2130
• 关键词: time-series; frequency-domain; phase

摘要

We explore the limits of the autoregressive (AR) sieve bootstrap, and show that its applicability extends well beyond the realm of linear time series as has been previously thought. In particular, for appropriate statistics, the AR-sieve bootstrap is valid for stationary processes possessing a general Wold-type autoregressive representation with respect to a white noise; in essence, this includes all stationary, purely nondeterministic processes, whose spectral density is everywhere positive. Our main theorem provides a simple and effective tool in assessing whether the AR-sieve bootstrap is asymptotically valid in any given situation. In effect, the large-sample distribution of the statistic in question must only depend on the first and second order moments of the process; prominent examples include the sample mean and the spectral density. As a counterexample, we show how the AR-sieve bootstrap is not always valid for the sample autocovariance even when the underlying process is linear.