Dynamics and sparsity in latent threshold factor models: A study in multivariate EEG signal processing
Dynamics and sparsity in latent threshold factor models: A study in multivariate EEG signal processing
复制标题
潜在阈值因子模型中的动态和稀疏性：多元脑电图信号处理的研究
DOI：
10.1214/17-bjps364
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
M. West
中科院分区：
文献类型：
--
作者：
Jouchi Nakajima;M. West
我们讨论多变量时间序列的贝叶斯分析与动态因子模型，利用时间适应稀疏性模型参数化通过潜在阈值方法。一个中心焦点是多个相互关联的系列对潜在的、动态的潜在因素过程的转移响应。模型超参数的结构化先验是动态潜在阈值有效性的关键，基于mcmc的计算使模型拟合和分析成为可能。来自实验精神病学的脑电图（EEG）数据的详细案例研究强调了时变矢量自回归和因子模型的潜在阈值扩展的使用。本研究探索了一类动态传递响应因子模型，扩展了多个EEG序列的先验贝叶斯建模，并突出了潜在阈值概念在多元非平稳时间序列分析中的实际应用。
We discuss Bayesian analysis of multivariate time series with dynamic factor models that exploit time-adaptive sparsity in model parametrizations via the latent threshold approach. One central focus is on the transfer responses of multiple interrelated series to underlying, dynamic latent factor processes. Structured priors on model hyper-parameters are key to the efficacy of dynamic latent thresholding, and MCMC-based computation enables model fitting and analysis. A detailed case study of electroencephalographic (EEG) data from experimental psychiatry highlights the use of latent threshold extensions of time-varying vector autoregressive and factor models. This study explores a class of dynamic transfer response factor models, extending prior Bayesian modeling of multiple EEG series and highlighting the practical utility of the latent thresholding concept in multivariate, non-stationary time series analysis.