The order determination for linear time-varying AR models

F. Nakajima, F. Kozin
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引用次数: 2

Abstract

We are often faced With the problem of estimating the number of parameters, as well as their values, for a model that will fit a given set of observations. When a family of models includes (or is assumed to include) the true system, the consistency of the estimated model has, for example, been studied recently by using the maximum entropy criterion as a measure of fit. However, to establish this test, stationarity and the knowledge of the p. d. f. for the observed data are required. In this paper we define a criterion of measure of fit which enables us to treat the non-stationary case without knowledge of the p.d.f. of the data or without specifying the p. d. f. of model outputs. Based on this criterion, we study the order determination problem for the linear time-varying AR models.
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线性时变AR模型的阶数确定
我们经常面临的问题是估计参数的数量,以及它们的值,为一个模型,将拟合一组给定的观测值。当一组模型包括(或被假定包括)真实系统时,估计模型的一致性已经,例如,最近通过使用最大熵准则作为拟合的度量来研究。然而,要建立这种测试,需要观测数据的平稳性和p.d.f.知识。在本文中,我们定义了一个拟合度量准则,使我们能够在不知道数据的p.d.f.或不指定模型输出的p.d.f.的情况下处理非平稳情况。在此基础上,研究了线性时变AR模型的阶数确定问题。
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