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首页> 外文期刊>International Journal of Modelling, Identification and Control >Application Of The Multi-model Partitioning Theory For Simultaneous Order And Parameter Estimation Of Multivariate Arma Models
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Application Of The Multi-model Partitioning Theory For Simultaneous Order And Parameter Estimation Of Multivariate Arma Models

机译：多模型划分理论在多元Arma模型同时阶和参数估计中的应用

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摘要

In this paper, a study on how to perform simultaneous order and parameter estimation of multivariate (MV) ARMA (autoregressive moving average) models under the presence of noise is addressed. The proposed method, which is computationally efficient, is an extension of a previously presented method for MV AR models and is based on the well established and widely applied multi-model partitioning theory. A series of computer simulations indicate that the method is infallible in selecting the correct model order in very few steps. The simultaneous estimation of the multivariate ARMA parameters is also another benefit of the proposed method. The results are compared with two other established order selection criteria namely Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). Finally, it is shown that the method is also successful in tracking model order changes, in real time.
机译：本文研究了在噪声存在下如何执行多元（MV）ARMA（自回归移动平均）模型的同时阶和参数估计的研究。所提出的方法在计算上是有效的，它是对先前提出的MV AR模型方法的扩展，并且基于已建立并广泛应用的多模型划分理论。一系列的计算机模拟表明，该方法在极少的步骤中选择正确的模型顺序是绝对可靠的。同时估计多元ARMA参数也是该方法的另一个好处。将结果与其他两个已建立的订单选择标准（即Akaike的信息标准（AIC）和Schwarz的贝叶斯信息标准（BIC））进行比较。最后，表明该方法还成功地实时跟踪模型顺序变化。

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