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Figure 5 MSE of ρ 2 estimation by using three copulas for actual value ρ 2= 0.1.
The simulation is done for the sample size N=10,000 and SNR values from 0 to +10 dB. Figure 2 MSE of m 1 estimation by using three copulas for ρ 1=0.5. Figure 3 MSE of ρ 1 estimation by using three copulas for actual value ρ 1 = 0.5.
The sample size and SNR values are the same as Figures 2 and 3. Figure 4 MSE of m 2 estimation by using three copulas for ρ 2= 0.1.
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Using eight precipitation indices, probabilistic characteristics of precipitation extremes were analyzed based on Copulas.
Several factors that influence the prediction with and without consideration of the statistical dependence between directional extreme wind speeds are discussed by using Gaussian copula model.
After discussing the copula concept and correlation modeling, a correlated channel is presented in the next section, and the parameters of the mentioned channel are estimated by using the copula function.
Never use one by yourself.
A normal or a Clayton copula-based estimator refers to the estimator in (36), in which the function f r(r) is calculated by using the normal or Clayton copula, respectively.
We can obtain parameters of wind speed sample each Copula function by using parameter estimation method.
Based on those functions, we can obtain parameters of wind speed sample under each Copula function by using parameters estimation method.
When the entries of channel matrix H are dependent, the copula theory helps us to extract the joint distribution of H by using (33).
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