Characterization of pre-idempotent Copulas

IF 0.6 Q4 STATISTICS & PROBABILITY
Wongtawan Chamnan, Songkiat Sumetkijakan
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引用次数: 0

Abstract

Abstract Copulas C C for which ( C t C ) 2 = C t C {({C}^{t}C)}^{2}={C}^{t}C are called pre-idempotent copulas, of which well-studied examples are idempotent copulas and complete dependence copulas. As such, we shall work mainly with the topology induced by the modified Sobolev norm, with respect to which the class {\mathcal{ {\mathcal R} }} of pre-idempotent copulas is closed and the class of factorizable copulas is a dense subset of {\mathcal{ {\mathcal R} }} . Identifying copulas with Markov operators on L 2 {L}^{2} , the one-to-one correspondence between pre-idempotent copulas and partial isometries is one of our main tools. In the same spirit as Darsow and Olsen’s work on idempotent copulas, we obtain an explicit characterization of pre-idempotent copulas, which is split into cases according to the atomicity of its associated σ \sigma -algebras, where the nonatomic case gives all factorizable copulas and the totally atomic case yields conjugates of ordinal sums of copies of the product copula.
前幂等copula的性质
摘要(C - t - C) 2 = C - t - C {({C}^{t}C)}^{2}={C}^{t}C的copulc - C被称为前幂等偶,其中研究较多的例子是幂等偶和完全相关copulc。因此,我们将主要研究由修正Sobolev范数引起的拓扑结构,在该拓扑结构下,前幂等copulas的类群∑{\mathcal{{\mathcal R}}}是封闭的,而可分解copulas的类群是∑{\mathcal{{\mathcal R}}}的密集子集。用l2 {L}^{2}上的马尔可夫算子识别联结,前幂等联结与部分等距的一一对应是我们的主要工具之一。与Darsow和Olsen关于幂等幂偶的研究精神相同,我们得到了前幂等幂偶的显式刻画,根据其相关σ \ σ -代数的原子性将其分成若干种情况,其中非原子情况给出了所有可分解的幂等幂偶,而全原子情况给出了乘积幂等幂偶的序和的共轭。
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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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