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Copula modeling is, in particular, useful when the marginal distributions of multivariate are easy to measure but their joint distribution is not. Compared to.
Copula can be used as a statistical tool to capture network traffic dependence. 2. Copula analysis discloses the range that stochastic network calculus can ...
Using copula theory, we show the range of performance bounds that SNC can achieve. With concrete numerical examples and real-world experiments, it is ...
Using copula theory, the range of performance bounds that SNC can achieve is shown and it is demonstrated that copula analysis offers a new opportunity for ...
... Copula models have been broadly used in the domain of financial analysis, for multivariate dependence modeling [21] and for time series modeling [22]. In ...
Extensions and generalization of copulas families are proposed for exponential, Rayleigh, Weibull, log-normal, Nakagami-m and Rician distributions.
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This paper presents algorithms for generating random variables for exponential/Rayleigh/Weibull, Nakagami-m and Rician copulas with any desired copula parameter ...
This paper aims to describe some simple statistical procedures currently employed to calibrate the copula functions to the financial market data.
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We compare our FCGGM with the existing functional Gaussian graphical model by simulations, and apply our method to an EEG data set to construct brain networks.
A copula is a multivariate cumulative distribution function for which the marginal probability distribution of each variable is uniform on the interval [0, 1].