AdaptSPEC: Adaptive Spectral Estimation for Nonstationary Time Series

• 成果类型: Article
• 署名作者: Rosen, Ori; Wood, Sally; Stoffer, David S.
• 署名单位: University of Texas System; University of Texas El Paso; University of Melbourne; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2012.716340
• 发表日期: 2012
• 页码: 1575-1589
• 关键词: bayesian mixture; AUTOREGRESSIVE MODELS; el-nino; Periodogram; inference; splines

摘要

We propose a method for analyzing possibly nonstationary time series by adaptively dividing the time series into an unknown but finite number of segments and estimating the corresponding Meal spectra by smoothing splines. The model is formulated in a Bayesian framework, and the estimation relies on reversible jump Markov chain Monte Carlo (RJMCMC) methods. For a given segmentation of the time,series, the likelihood function is approximated via a product of local Whittle likelihoods. Thus, no parametric assumption is made about the process underlying the time series. The number and lengths of the segments are assumed unknown and may change from one MCMC iteration to another. The frequentist properties of the method are investigated by simulation, and applications to electroencephalogram and the El Nino Southern Oscillation phenomenon are described in detail.