.png){kind=icon}
返回
Bayesian Spectral Modeling for Multiple Time Series
.png){kind=icon}
OA
AI
DOI：10.1080/01621459.2018.1520114.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We develop a novel Bayesian modeling approach to spectral density estimation for multiple time series. The log-periodogram distribution for each series is modeled as a mixture of Gaussian distributions with frequency-dependent weights and mean functions. The implied model for the log-spectral density is a mixture of linear mean functions with frequency-dependent weights. The mixture weights are built through successive differences of a logit-normal distribution function with frequency-dependent parameters. Building from the construction for a single spectral density, we develop a hierarchical extension for multiple time series. Specifically, we set the mean functions to be common to all spectral densities and make the weights specific to the time series through the parameters of the logit-normal distribution. In addition to accommodating flexible spectral density shapes, a practically important feature of the proposed formulation is that it allows for ready posterior simulation through a Gibbs sampler with closed form full conditional distributions for all model parameters. The modeling approach is illustrated with simulated datasets and used for spectral analysis of multichannel electroencephalographic recordings, which provides a key motivating application for the proposed methodology.
Keyword:
Gaussian mixtures
Log-periodogram
Markov chain Monte Carlo
Multichannel electroencephalography
Whittle likelihood
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
J
IF:
3
论文数:
5.2K
被引数:
4.8W
机构
引用论文
分享收藏分享收藏
APPROXIMATION OF CONDITIONAL DENSITIES BY SMOOTH MIXTURES OF REGRESSIONS通过平滑的回归混合来逼近条件密度.png)
ANNALS OF STATISTICSOAAI
ANNALS OF STATISTICSIF3.7
OAAI分享收藏
查看更多内容