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Interpretation of continuous-time autoregressive processes as random exponential splines

机译:将连续时间自回归过程解释为随机指数样条

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We consider the class of continuous-time autoregressive (CAR) processes driven by (possibly non-Gaussian) Lévy white noises. When the excitation is an impulsive noise, also known as compound Poisson noise, the associated CAR process is a random non-uniform exponential spline. Therefore, Poisson-type processes are relatively easy to understand in the sense that they have a finite rate of innovation. We show in this paper that any CAR process is the limit in distribution of a sequence of CAR processes driven by impulsive noises. Hence, we provide a new interpretation of general CAR processes as limits of random exponential splines. We illustrate our result with simulations.
机译:我们考虑由(可能是非高斯)Levy白噪声驱动的连续时间自回归(CAR)过程的类型。当激励是脉冲噪声(也称为复合泊松噪声)时,相关的CAR过程就是随机的非均匀指数样条。因此,就泊松型过程具有有限的创新率而言,它相对容易理解。我们在本文中证明,任何CAR过程都是由脉冲噪声驱动的一系列CAR过程分布的极限。因此,我们对一般CAR过程作为随机指数样条的限制提供了新的解释。我们通过仿真来说明我们的结果。

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