Bayesian Dynamic Tensor Regression
提出一种新的线性自回归张量过程模型，利用PARAFAC低秩分解实现参数简约化，并通过贝叶斯推断进行收缩估计，适用于多层网络时间序列的冲击传播分析。
High- and multi-dimensional array data are becoming increasingly available. They admit a natural representation as tensors and call for appropriate statistical tools. We propose a new linear autoregressive tensor process (ART) for tensor-valued data, that encompasses some well-known time series models as special cases. We study its properties and derive the associated impulse response function. We exploit the PARAFAC low-rank decomposition for providing a parsimonious parameterization and develop a Bayesian inference allowing for shrinking effects. We apply the ART model to time series of multilayer networks and study the propagation of shocks across nodes, layers and time.