TY - JOUR
T1 - Copulas for statistical signal processing (part II)
T2 - simulation, optimal selection and practical applications
AU - Zeng, Xuexing
AU - Ren, Jinchang
AU - Sun, Meijun
AU - Marshall, Stephen
AU - Durrani, Tariq
PY - 2014/1
Y1 - 2014/1
N2 - 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
AB - 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
KW - copulas
KW - statistical signal processing
KW - copula random variables generation
KW - optimal copula selection
UR - http://www.sciencedirect.com/science/article/pii/S0165168413002855
U2 - 10.1016/j.sigpro.2013.07.006
DO - 10.1016/j.sigpro.2013.07.006
M3 - Article
VL - 94
SP - 681
EP - 690
JO - Signal Processing
JF - Signal Processing
SN - 0165-1684
ER -