论文标题
动态潜在因子模型中的贝叶斯计算
Bayesian Computation in Dynamic Latent Factor Models
论文作者
论文摘要
用于过滤和预测分析的贝叶斯计算是针对广泛的动态模型开发的。通过引入新型的copula构造，在非高斯，非线性多元时间序列模型中扩展此类分析的能力可以提高，以序列滤波的顺序滤波组合的动态广义线性模型的耦合集。新的Copula方法集成到最近引入的多尺度模型中，其中单变量时间序列通过非线性形式耦合，涉及代表跨系列关系的动态潜在因素。最终的方法在在线贝叶斯计算中提供了巨大的加速，以在这种广泛的，灵活的多元模型类别中进行顺序过滤和预测。非线性时间序列的非线性模型中的两个示例的非负计数序列相对于现有基于仿真的方法表明了巨大的计算效率，同时定义了相似的过滤和预测结果。
Bayesian computation for filtering and forecasting analysis is developed for a broad class of dynamic models. The ability to scale-up such analyses in non-Gaussian, nonlinear multivariate time series models is advanced through the introduction of a novel copula construction in sequential filtering of coupled sets of dynamic generalized linear models. The new copula approach is integrated into recently introduced multiscale models in which univariate time series are coupled via nonlinear forms involving dynamic latent factors representing cross-series relationships. The resulting methodology offers dramatic speed-up in online Bayesian computations for sequential filtering and forecasting in this broad, flexible class of multivariate models. Two examples in nonlinear models for very heterogeneous time series of non-negative counts demonstrate massive computational efficiencies relative to existing simulation-based methods, while defining similar filtering and forecasting outcomes.
