Mike West 时间序列数据集S Carinae 星的数据.pdfVIP

Mike West 时间序列数据集:S. Carinae 星的数据 (Time series data sets in Mike West:S. Carinae star data) 数据介绍： 1189 10-day mean light intensity recordings on this variable star, as analyzed and referenced in Huerta and Wests paper published in J. Time Series Anal., , 20:401-406, 1999 关键词： 数据格式： TEXT 数据详细介绍： S. Carinae star data 1189 10-day mean light intensity recordings on this variable star, as analyzed and referenced in Huerta and Wests paper published in J. Time Series Anal., , 20:401-406, 1999 BAYESIAN INFERENCE ON PERIODICITIES AND COMPONENT SPECTRAL STRUCTURE IN TIME SERIES Gabriel Huerta and Mike West We detail and illustrate time series analysis and spectral inference in autoregressive models with a focus on the underlying latent structure and time series decompositions. A novel class of priors on parameters of latent components leads to a new class of smoothness priors on autoregressive coefficients, provides for formal inference on model order, including very high order models, and leads to the incorporation of uncertainty about model order into summary inferences. The class of prior models also allows for subsets of unit roots, and hence leads to inference on sustained though stochastically time-varying periodicities in time series. Applications to analysis of the frequency composition of time series, in both time and spectral domains, is illustrated in a study of a time series from astronomy. This analyses demonstrates the impact and utility of the new class of priors in addressing model order uncertainty and in allowing for unit root structure. Time domain decomposition of a time series into estimated latent components provides an important alternative view of the component spectral characteristics of a series. In addition, our data analysis illustrates the utility of the smoothness prior and all