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Order Estimation and Discrimination Between Stationary and Time-Varying (TVAR) Autoregressive Models

机译:平稳和时变(TVAR)自回归模型的阶估计和区分

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For a set of T independent observations of the same N-variate correlated Gaussian process, we derive a method of estimating the order of an autoregressive (AR) model of this process, regardless of its stationary or time-varying nature. We also derive a test to discriminate between stationary AR models of order m,AR(m), and time-varying autoregressive models of order m,TVAR(m). We demonstrate that within this technique the number T of independent identically distributed data samples required for order estimation and discrimination just exceeds the maximum possible order mmax, which in many cases is significantly fewer than the dimension of the problem N
机译:对于同一N变量相关高斯过程的一组T独立观测,我们推导了一种估计该过程的自回归(AR)模型顺序的方法,而不论其平稳性或时变性质如何。我们还推导了一个测试来区分阶数为m,AR(m)的平稳AR模型和阶数为m,TVAR(m)的时变自回归模型。我们证明,在这种技术中,阶次估计和判别所需的独立的,均匀分布的数据样本的数量T仅超过最大可能阶数mmax,在许多情况下,该阶数显着小于问题N

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