论文标题
因果媒介自动进度通过协方差和订单选择增强
Causal Vector Autoregression Enhanced with Covariance and Order Selection
论文作者
论文摘要
引入了因果媒介自回旋（CVAR）模型，以用于弱固定的多元过程，结合了同时组件的递归有向图形模型和纵向的矢量自回旋模型。块状分解块具有不同的块大小来求解模型方程并沿有向无环图（DAG）估算路径系数。如果DAG是可分解的，即零在其邻接矩阵中形成可还原的零模式（RZP），则应用协方差选择将零分配给相应的路径系数。还考虑了现实生活中的应用程序，其中适合CVAR $（P）$模型的最佳订单$ P \ ge 1 $，使用各种信息标准执行订单选择。
A causal vector autoregressive (CVAR) model is introduced for weakly stationary multivariate processes, combining a recursive directed graphical model for the contemporaneous components and a vector autoregressive model longitudinally. Block Cholesky decomposition with varying block sizes is used to solve the model equations and estimate the path coefficients along a directed acyclic graph (DAG). If the DAG is decomposable, i.e. the zeros form a reducible zero pattern (RZP) in its adjacency matrix, then covariance selection is applied that assigns zeros to the corresponding path coefficients. Real life applications are also considered, where for the optimal order $p\ge 1$ of the fitted CVAR$(p)$ model, order selection is performed with various information criteria.
