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arXiv 1008.2697math.PRmath.STstat.TH
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含不定常数据的经验过程的CLT

A CLT for empirical processes involving time-dependent data

James Kuelbs, Thomas Kurtz, Joel Zinn

摘要:

对于随机过程$\{X_t:t\in E\}$,建立了基于$\{I_{X_t\le y}-\operatorname{Pr}(X_t\le y):t\in E,y\in\mathbb{R}\}$的经验过程在$t\in E,y\in\mathbb{R}$中一致满足CLT的充分条件.我们的主要结果的推论包括经典过程的CLT举行的例子,我们还表明,它失败的布朗运动束缚在零和$E=[0,1]$。

英文摘要:

For stochastic processes $\{X_t:t\in E\}$, we establish sufficient conditions for the empirical process based on $\{I_{X_t\le y}-\operatorname{Pr}(X_t\le y):t\in E,y\in\mathbb{R}\}$ to satisfy the CLT uniformly in $t\in E,y\in\mathbb{R}$. Corollaries of our main result include examples of classical processes where the CLT holds, and we also show that it fails for Brownian motion tied down at zero and $E=[0,1]$.

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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