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Bootstrap for the second-order analysis of Poisson-sampled almost periodic processes

机译:Bootstrap用于对Poisson采样的几乎周期性过程进行二阶分析

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In this paper we consider a continuous almost periodically correlated process ${X(t),tinmathbb{R}}$ that is observed at the jump moments of a stationary Poisson point process ${N(t),tgeq0}$. The processes ${X(t),tinmathbb{R}}$ and ${N(t),tgeq0}$ are assumed to be independent. We define the kernel estimators of the Fourier coefficients of the autocovariance function of $X(t)$ and investigate their asymptotic properties. Moreover, we propose a bootstrap method that provides consistent pointwise and simultaneous confidence intervals for the considered coefficients. Finally, to illustrate our results we provide a simulated data example.
机译:在本文中,我们考虑了一个连续的几乎周期性相关的过程$ {X(t),t in mathbb {R} } $,该过程在固定的Poisson点过程$ {N(t)的跳跃时刻观察到,t geq0 } $。假定进程$ {X(t),t in mathbb {R} } $和$ {N(t),t geq0 } $是独立的。我们定义$ X(t)$自协方差函数的Fourier系数的核估计量,并研究它们的渐近性质。此外,我们提出了一种自举方法,为考虑的系数提供一致的逐点和同时置信区间。最后,为了说明我们的结果,我们提供了一个模拟数据示例。

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