ANOPOW FOR REPLICATED NONSTATIONARY TIME SERIES IN EXPERIMENTS

• 成果类型: Article
• 署名作者: Li, Zeda; Yue, Yu (ryan); Bruce, Scott A.
• 署名单位: City University of New York (CUNY) System; Baruch College (CUNY); Texas A&M University System; Texas A&M University College Station
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/23-AOAS1791
• 发表日期: 2024
• 页码: 328-349
• 关键词: dependent spectral-analysis; wavelet estimation; models

摘要

We propose a novel analysis of power (ANOPOW) model for analyzing replicated nonstationary time series commonly encountered in experimental studies. Based on a locally stationary ANOPOW Cramer spectral representation, the proposed model can be used to compare the second -order timevarying frequency patterns among different groups of time series and to estimate group effects as functions of both time and frequency. Formulated in a Bayesian framework, independent two-dimensional second -order random walk (RW2D) priors are assumed on each of the time -varying functional effects for flexible and adaptive smoothing. A piecewise stationary approximation of the nonstationary time series is used to obtain localized estimates of time -varying spectra. Posterior distributions of the time -varying functional group effects are then obtained via integrated nested Laplace approximations (INLA) at a low computational cost. The large -sample distribution of local periodograms can be appropriately utilized to improve estimation accuracy since INLA allows modeling of data with various types of distributions. The usefulness of the proposed model is illustrated through two real -data applications: analyses of seismic signals and pupil diameter time series in children with attention deficit hyperactivity disorder. Simulation studies, Supplementary Material (Li, Yue and Bruce (2024a)), and R code (Li, Yue and Bruce (2024b)) for this article are also available.