Real‐time covariance estimation for the local level model
Real‐time covariance estimation for the local level model
复制标题
局部模型的实时协方差估计
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
K. Triantafyllopoulos
中科院分区：
文献类型：
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作者：
K. Triantafyllopoulos
本文发展了多元局部水平模型的在线推理，重点放在创新的协方差估计上。我们评估了逆Wishart先验分布在这种情况下的应用，发现它过于限制，因为观测和状态创新的序列相关结构被迫相同。我们推广逆Wishart分布，以允许更方便的相关结构，但仍然保留近似共轭。我们证明了新分布的一些相关结果，并开发了近似贝叶斯推理，它可以同时预测时间序列数据和估计模型创新的协方差。我们提供了关于时间序列水平稳态的结果，这些结果被部署以实现计算节省。通过蒙特卡罗实验，我们将所提出的方法与现有的估计方法进行了比较。给出了一个由工业过程生产数据组成的实际数据的实例。
This article develops on‐line inference for the multivariate local level model, with the focus being placed on covariance estimation of the innovations. We assess the application of the inverse Wishart prior distribution in this context and find it too restrictive since the serial correlation structure of the observation and state innovations are forced to be the same. We generalize the inverse Wishart distribution to allow for a more convenient correlation structure, but still retaining approximate conjugacy. We prove some relevant results for the new distribution and we develop approximate Bayesian inference, which allows simultaneous forecasting of time series data and estimation of the covariance of the innovations of the model. We provide results on the steady state of the level of the time series, which are deployed to achieve computational savings. Using Monte Carlo experiments, we compare the proposed methodology with existing estimation procedures. An example with real data consisting of production data from an industrial process is given.