论文标题
使用分数边缘伪可能的稀疏矢量自回归模型的高维结构学习
High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood
论文作者
论文摘要
通过最小二乘或最大似然估计，从多变量时间序列中学习矢量自回旋模型。这些方法通常假设一个完全连接的模型，该模型对模型结构没有直接见解，并且可能导致对参数的高度嘈杂的估计。由于这些局限性，人们对通过惩罚回归产生稀疏估计的方法的兴趣越来越大。但是，此类方法在计算上是密集型的，当模型中的变量数量增加时，可能会变得非常耗时。在本文中，我们通过结合分数边缘可能性和伪样的样子来采用大概的贝叶斯方法来解决学习问题。我们提出了一种新颖的方法PLVAR，它既比基于惩罚回归的最新方法更快又产生更准确的估计。我们证明了PLVAR估计器的一致性，并证明了该方法在模拟和现实世界中的有吸引力的性能。
Learning vector autoregressive models from multivariate time series is conventionally approached through least squares or maximum likelihood estimation. These methods typically assume a fully connected model which provides no direct insight to the model structure and may lead to highly noisy estimates of the parameters. Because of these limitations, there has been an increasing interest towards methods that produce sparse estimates through penalized regression. However, such methods are computationally intensive and may become prohibitively time-consuming when the number of variables in the model increases. In this paper we adopt an approximate Bayesian approach to the learning problem by combining fractional marginal likelihood and pseudo-likelihood. We propose a novel method, PLVAR, that is both faster and produces more accurate estimates than the state-of-the-art methods based on penalized regression. We prove the consistency of the PLVAR estimator and demonstrate the attractive performance of the method on both simulated and real-world data.
