bivariate distribution

bivariate distribution

[bī¦ver·ē·ət ‚dis·trə′byü·shən]
(statistics)
The joint distribution of a pair of variates for continuous or discontinuous data.
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17] have used the Macdonald distribution to construct a new bivariate distribution which they call Macdonald-gamma distribution.
It could be concluded that CMLC method is reasonable and performs best in bivariate distribution.
A bivariate copula is a bivariate distribution function with both univariate margins distributed as U (0,1).
The degrees of freedom parameter, v, is the most important determinant of the asymptotic tail dependence for this bivariate distribution.
Nevertheless, homogeneity was achieved in the bivariate distribution in the three data sets from each of the measuring methods (i.
2] are univariate distributions then we can find a bivariate distribution function H such that [F.
Caillault and Guegan consider a non parametric approach using the copula method, calculating the bivariate distribution function of the portfolio in a dynamic way, and introducing a greater flexibility for the policy management of the bank supervisors.
4] and TSH, and therefore the reference limits derived from the bivariate distribution for each parameter are mutually dependent.
The bivariate distribution function of two independent extreme action effects may be presented as their conventional joint distribution function with the mean [E.
Given a set of N independent and identically distributed (iid) bivariate observations from a bivariate distribution with copula [C.