Copulas for statistical signal processing (Part II): Simulation, optimal selection and practical applications
Copulas for statistical signal processing (Part II): Simulation, optimal selection and practical applications
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DOI：
10.1016/j.sigpro.2013.07.006
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Xuexing Zeng;Jinchang Ren;Meijun Sun;S. Marshall;T. Durrani
中科院分区：
文献类型：
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作者：
Xuexing Zeng;Jinchang Ren;Meijun Sun;S. Marshall;T. Durrani
本文利用直接条件累积分布函数法和复高斯分布法，给出了具有任意期望参数的指数/瑞利/威布尔、Nakagami-m和ric_copula随机变量的生成算法。此外，还提出了一种新的最优联结选择方法，该方法基于给定联结序列的最优联结密度互信息与相应的二元分布互信息最接近的准则。对应的二元分布就是用来推导这个联结式的二元分布。将赤池信息准则（AIC）和贝叶斯信息准则（BIC）与所提出的基于互信息的最优联结选择准则进行了比较。在实际应用中，利用Nakagami-m、指数/Rayleigh/Weibull和具有不同边际分布的fourier copula结合多样性进行双分支选择，进一步验证了copula算法的有效性。
This paper presents algorithms for generating random variables for exponential/Rayleigh/Weibull, Nakagami-m and Rician copulas with any desired copula parameter(s), using the direct conditional cumulative distribution function method and the complex Gaussian distribution method. Moreover, a novel method for optimal copula selection is also proposed, based on the criterion that for a given series of copulas, the optimal copula will have its copula density based mutual information closest to the corresponding bivariate distribution based mutual information. The corresponding bivariate distribution is the bivariate distribution that is used to derive this copula. Akaike information criterion (AIC) and Bayes’ information criterion (BIC) are compared with the proposed mutual information based criterion for optimal copula selection. In addition, several case studies are also presented to further validate the effectiveness of the copulas, which include dual branch selection combining diversity using Nakagami-m, exponential/Rayleigh/Weibull and Rician copulas with different marginal distributions as in real applications.