arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1609.01807math.PRstat.CO
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精确高效地模拟重尾无穷级数的尾部概率

Exact and efficient simulation of tail probabilities of heavy-tailed infinite series

Henrik Hult, Sandeep Juneja, Karthyek Murthy

摘要:

当随机变量$X_n$是重尾的时,我们提出了一种计算无穷级数$S = \sum_{n \geq 1} a_n X_n$尾概率的有效模拟算法.由于$S$是无限多个随机变量的和,因此任何仅模拟固定的、无限多个随机变量后停止的模拟算法都可能引入偏差。我们克服了这一挑战,通过重写尾概率的利益作为一个随机数的伸缩项的总和,并随后开发条件蒙特卡洛为基础的低方差模拟估计每个伸缩项。由此产生的算法被证明导致估计,a)没有偏见,和b)只需要一个固定的,有限数量的重复,无论如何罕见的尾部概率的兴趣。因此,通过结合传统的方差减少技术,如条件蒙特卡罗与最近使用的辅助随机化,以消除偏见的多级类型表示,我们开发了一个有效的和无偏的模拟算法的尾部概率$S$。这些有许多应用,包括在金融时间序列和随机递归方程的精算风险和人口生物学模型中产生的分析。

英文摘要:

We develop an efficient simulation algorithm for computing the tail probabilities of the infinite series $S = \sum_{n \geq 1} a_n X_n$ when random variables $X_n$ are heavy-tailed. As $S$ is the sum of infinitely many random variables, any simulation algorithm that stops after simulating only fixed, finitely many random variables is likely to introduce a bias. We overcome this challenge by rewriting the tail probability of interest as a sum of a random number of telescoping terms, and subsequently developing conditional Monte Carlo based low variance simulation estimators for each telescoping term. The resulting algorithm is proved to result in estimators that a) have no bias, and b) require only a fixed, finite number of replications irrespective of how rare the tail probability of interest is. Thus, by combining a traditional variance reduction technique such as conditional Monte Carlo with more recent use of auxiliary randomization to remove bias in a multi-level type representation, we develop an efficient and unbiased simulation algorithm for tail probabilities of $S$. These have many applications including in analysis of financial time-series and stochastic recurrence equations arising in models in actuarial risk and population biology.

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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