具有共同任意边界的三重独立随机变量的经典中心极限定理的反例

IF 0.6 Q4 STATISTICS & PROBABILITY Dependence Modeling Pub Date : 2021-01-01 DOI:10.1515/demo-2021-0120
Guillaume Beaulieu, P. L. D. Micheaux, Frédéric Ouimet
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引用次数: 0

摘要

摘要本文给出了构造具有共同但任意的边际分布F(满足非常温和的条件)的三向独立随机变量序列的一般方法。对于两个特定的序列,我们用封闭形式得到了样本均值的渐近分布。它是非高斯的(并且取决于F的具体选择)。这使我们能够说明经典中心极限定理(CLT)在三重独立性下的“失败”程度。我们的方法很简单,也可以用于创建任何整数K,新的K元独立序列,这些序列不是相互独立的。对于K[4]。[tf],似乎使用我们的方法创建的序列确实验证了CLT,我们启发式地解释了为什么会出现这种情况。
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Counterexamples to the classical central limit theorem for triplewise independent random variables having a common arbitrary margin
Abstract We present a general methodology to construct triplewise independent sequences of random variables having a common but arbitrary marginal distribution F (satisfying very mild conditions). For two specific sequences, we obtain in closed form the asymptotic distribution of the sample mean. It is non-Gaussian (and depends on the specific choice of F). This allows us to illustrate the extent of the ‘failure’ of the classical central limit theorem (CLT) under triplewise independence. Our methodology is simple and can also be used to create, for any integer K, new K-tuplewise independent sequences that are not mutually independent. For K [four.tf], it appears that the sequences created using our methodology do verify a CLT, and we explain heuristically why this is the case.
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来源期刊
Dependence Modeling
Dependence Modeling STATISTICS & PROBABILITY-
CiteScore
1.00
自引率
0.00%
发文量
18
审稿时长
12 weeks
期刊介绍: The journal Dependence Modeling aims at providing a medium for exchanging results and ideas in the area of multivariate dependence modeling. It is an open access fully peer-reviewed journal providing the readers with free, instant, and permanent access to all content worldwide. Dependence Modeling is listed by Web of Science (Emerging Sources Citation Index), Scopus, MathSciNet and Zentralblatt Math. The journal presents different types of articles: -"Research Articles" on fundamental theoretical aspects, as well as on significant applications in science, engineering, economics, finance, insurance and other fields. -"Review Articles" which present the existing literature on the specific topic from new perspectives. -"Interview articles" limited to two papers per year, covering interviews with milestone personalities in the field of Dependence Modeling. The journal topics include (but are not limited to):  -Copula methods -Multivariate distributions -Estimation and goodness-of-fit tests -Measures of association -Quantitative risk management -Risk measures and stochastic orders -Time series -Environmental sciences -Computational methods and software -Extreme-value theory -Limit laws -Mass Transportations
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