论文标题
参数协方差最大似然估计
Parameter-Covariance Maximum Likelihood Estimation
论文作者
论文摘要
线性时间序列建模以使用纯回归模型的使用为主导，即使合并移动平均成分可以大大改善简约性。我们提出了一个介于矢量 - ARMA系统识别的凸公式，该公式尊重这一基本属性，从而授予访问凸面编程提供的良好属性。识别过程纯粹是在时间域中完成的，该时间域可以通过制度切换来适应非平稳性。作为概念证明，我们提出了实验结果，证明了该凸面程序正在行动。接下来，我们将展示如何调整期望最大化算法以支持制度切换行为。
Linear time series modelling is dominated by the use of purely autoregressive models even though incorporating moving average components can greatly improve parsimony. We present a convex formulation for vector-ARMA system identification which respects this fundamental property, thus granting access to the nice properties afforded by convex programming. The identification procedure is done purely in the time domain which can accommodate non-stationarity through regime switching. As a proof of concept, we present experimental results demonstrating this convex program in action. Next, we show how to adapt the expectation-maximization algorithm to support regime switching behavior.
