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arXiv 1503.08848math.PR
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在不进行拉普拉斯变换的情况下得到了条件Berry-Esseen界和条件大偏差结果。线性探测散列法的应用

A conditional Berry-Esseen bound and a conditional large deviation result without Laplace transform. Application to hashing with linear probing

Thierry Klein, Agnès Lagnoux, Pierre Petit

摘要:

\noindent研究了独立同分布随机变量之和在独立同分布整数值随机变量之和的条件下的渐近行为。我们证明了Berry-Esseen界在一般设置和一个大偏差结果时,拉普拉斯变换的基础分布是不是在零的邻域内定义。然后,我们提出了几个组合的应用。特别是,我们证明了一个大偏差的结果与线性探测散列模型。

英文摘要:

\noindent We study the asymptotic behavior of a sum of independent and identically distributed random variables conditioned by a sum of independent and identically distributed integer-valued random variables. We prove a Berry-Esseen bound in a general setting and a large deviation result when the Laplace transform of the underlying distribution is not defined in a neighborhood of zero. Then we present several combinatorial applications. In particular, we prove a large deviation result for the model of hashing with linear probing.

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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