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arXiv 1304.7637math.PR
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马尔可夫尾链

Markov tail chains

Anja Janßen, Johan Segers

摘要:

已知具有正则变化平稳边缘分布和渐近线性行为的单变量马尔可夫链的极值呈现乘法随机游动结构,称为尾链。本文将这一事实推广到R^d中具有多元正则变化边缘分布的马尔可夫链。分析了前向尾过程和后向尾过程,指出它们通过一种伴随关系相互决定。在更广泛的背景下,可以看到,即使对于非马尔可夫的底层过程,马尔可夫的前向尾链也总是意味着后向尾链也是马尔可夫的。我们详细分析了由此产生的类的限制过程。应用该理论得到了以极值事件为条件的一元和多元随机差分方程的过去和未来的渐近分布。

英文摘要:

The extremes of a univariate Markov chain with regulary varying stationary marginal distribution and asymptotically linear behavior are known to exhibit a multiplicative random walk structure called the tail chain. In this paper, we extend this fact to Markov chains with multivariate regularly varying marginal distribution in R^d. We analyze both the forward and the backward tail process and show that they mutually determine each other through a kind of adjoint relation. In a broader setting, it will be seen that even for non-Markovian underlying processes a Markovian forward tail chain always implies that the backward tail chain is Markovian as well. We analyze the resulting class of limiting processes in detail. Applications of the theory yield the asymptotic distribution of both the past and the future of univariate and multivariate stochastic difference equations conditioned on an extreme event.

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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