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arXiv 0901.3493math.PR
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瓮分配模型中孤立球的正态逼近

Normal approximation for isolated balls in an urn allocation model

Mathew D. Penrose

摘要:

考虑将$n$球随机扔到$m$瓮中,每个球以$p_i$的概率落在瓮$i$中。令$S$为单例的结果数量,即,只有一个球的瓮。我们给出了从$S$到正态分布的Kolmogorov距离的误差界,并估计了其方差。这些表明,如果$n$,$m$和$(p_i, 1 \leq i \leq m)$以这样一种方式变化,即$\sup_i p_i = O(n^{-1})$,那么$S$满足CLT当且仅当$n^2 \sum_i p_i^2$趋于无穷大,并证明了在这种情况下CLT的最佳收敛速度。在均匀的情况下,$(p_i \equiv m^{-1}) with $ m $ and $ n$增长的比例,我们提供了更好的渐近常数的界限。误差界的证明是基于Stein的方法,通过尺寸偏置耦合。

英文摘要:

Consider throwing $n$ balls at random into $m$ urns, each ball landing in urn $i$ with probability $p_i$. Let $S$ be the resulting number of singletons, i.e., urns containing just one ball. We give an error bound for the Kolmogorov distance from $S$ to the normal, and estimates on its variance. These show that if $n$, $m$ and $(p_i, 1 \leq i \leq m)$ vary in such a way that $\sup_i p_i = O(n^{-1})$, then $S$ satisfies a CLT if and only if $n^2 \sum_i p_i^2$ tends to infinity, and demonstrate an optimal rate of convergence in the CLT in this case. In the uniform case $(p_i \equiv m^{-1}) with $m$ and $n$ growing proportionately, we provide bounds with better asymptotic constants. The proof of the error bounds are based on Stein's method via size-biased couplings.

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