arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1707.09490math.PR
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弱平稳过程的建模

On modeling weakly stationary processes

Lauri Viitasaari, Pauliina Ilmonen

摘要:

在这篇文章中,我们表明,一般类弱平稳时间序列可以建模高斯从属过程。我们证明了,对于任何给定的弱平稳时间序列$(z_t)_{z\in\mathbb{N}}$与给定的相等的一维边缘分布,人们总是可以构建一个函数$f$和高斯过程$(X_t)_{t\in\mathbb{N}}$,使$\left(f(X_t)\right)_{t\in\mathbb{N}}$具有相同的边缘分布,并渐近地,相同的自协方差函数作为$(z_t)_{t\in\mathbb{N}}$。因此,我们利用高斯从属过程的极限定理的丰富理论,得到了均值和自协方差估计的渐近分布。这突出了高斯从属过程在一般弱平稳时间序列建模中的作用。我们比较我们的方法,标准的线性模型,并表明,我们的模型是更灵活的,需要较弱的假设。

英文摘要:

In this article, we show that a general class of weakly stationary time series can be modeled applying Gaussian subordinated processes. We show that, for any given weakly stationary time series $(z_t)_{z\in\mathbb{N}}$ with given equal one-dimensional marginal distribution, one can always construct a function $f$ and a Gaussian process $(X_t)_{t\in\mathbb{N}}$ such that $\left(f(X_t)\right)_{t\in\mathbb{N}}$ has the same marginal distributions and, asymptotically, the same autocovariance function as $(z_t)_{t\in\mathbb{N}}$. Consequently, we obtain asymptotic distributions for the mean and autocovariance estimators by using the rich theory on limit theorems for Gaussian subordinated processes. This highlights the role of Gaussian subordinated processes in modeling general weakly stationary time series. We compare our approach to standard linear models, and show that our model is more flexible and requires weaker assumptions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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