Bayesian Inference in Cyclical Component Dynamic Linear Models
研究了带有时变周期成分的动态线性模型，用于分析具有持续但时变周期行为的时间序列，并利用吉布斯采样等随机模拟方法进行贝叶斯推断，适用于非平稳时间序列的周期波长、振幅和相位变化分析。
Abstract Dynamic linear models (DLM's) with time-varying cyclical components are developed for the analysis of time series with persistent though time-varying cyclical behavior. The development covers inference on wavelengths of possibly several persistent cycles in nonstationary time series, permitting explicit time variation in amplitudes and phases of component waveforms, decomposition of stochastic inputs into purely observational noise and innovations that impact on the waveform characteristics, with extensions to incorporate ranges of (time-varying) time series and regression terms wihin the standard DLM context. Bayesian inference via iterative stochastic simulation methods is developed and illustrated. Some indications of model extensions and generalizations are given. In addition to the specific focus on cyclical component models, the development provides the basis for Bayesian inference, via stochastic simulation, for state evolution matrix parameters and variance components in DLM's, building on recent work on Gibbs sampling for state vectors in such models by other authors.