A generalized Kubilius-Barban-Vinogradov bound for prime multiplicities

IF 0.6 4区 数学 Q4 STATISTICS & PROBABILITY Alea-Latin American Journal of Probability and Mathematical Statistics Pub Date : 2021-11-14 DOI:10.30757/alea.v20-27
Louis H. Y. Chen, Arturo Jaramillo, Xiaochuan Yang
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Abstract

We present an assessment of the distance in total variation of \textit{arbitrary} collection of prime factor multiplicities of a random number in $[n]=\{1,\dots, n\}$ and a collection of independent geometric random variables. More precisely, we impose mild conditions on the probability law of the random sample and the aforementioned collection of prime multiplicities, for which a fast decaying bound on the distance towards a tuple of geometric variables holds. Our results generalize and complement those from Kubilius et al. which consider the particular case of uniform samples in $[n]$ and collection of"small primes". As applications, we show a generalized version of the celebrated Erd\"os Kac theorem for not necessarily uniform samples of numbers.
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素数多重的广义Kubilius-Barban-Vinogradov界
我们给出了$[n]=\{1,\dots,n}$中随机数的素数乘性集合和独立几何随机变量集合的总变差距离的评估。更准确地说,我们对随机样本的概率律和前面提到的素数乘性集合施加了温和的条件,对于这些条件,几何变量元组的距离上的快速衰减界成立。我们的结果推广和补充了Kubilius等人的结果。他们考虑了$[n]$中一致样本和“小素数”集合的特殊情况。作为应用,我们展示了著名的Erd\“os-Kac定理的广义版本,用于不一定一致的数字样本。
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来源期刊
CiteScore
1.10
自引率
0.00%
发文量
48
期刊介绍: ALEA publishes research articles in probability theory, stochastic processes, mathematical statistics, and their applications. It publishes also review articles of subjects which developed considerably in recent years. All articles submitted go through a rigorous refereeing process by peers and are published immediately after accepted. ALEA is an electronic journal of the Latin-american probability and statistical community which provides open access to all of its content and uses only free programs. Authors are allowed to deposit their published article into their institutional repository, freely and with no embargo, as long as they acknowledge the source of the paper. ALEA is affiliated with the Institute of Mathematical Statistics.
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