The Galambos copula (Joe, 2014, p. 174) is
\mathbf{C}_{Θ}(u,v) = \mathbf{GL}(u,v) = uv\,\mathrm{exp}\{[x^{-Θ} + y^{-Θ}]^{-1/Θ}\}\mbox{,}
where Θ \in [0, ∞), x = -\log(u), and y = -\log(v). As Θ \rightarrow 0^{+}, the copula limits to independence (\mathbf{Π}; P
) and as Θ \rightarrow ∞, the copula limits to perfect association (\mathbf{M}; M
).
The copula here is a bivariate extreme value copula (BEV), and the parameter Θ requires numerical methods.
1 |
u |
Nonexceedance probability u in the X direction; |
v |
Nonexceedance probability v in the Y direction; |
para |
A vector (single element) of parameters—the Θ parameter of the copula; and |
... |
Additional arguments to pass. |
Value(s) for the copula are returned.
W.H. Asquith
Joe, H., 2014, Dependence modeling with copulas: Boca Raton, CRC Press, 462 p.
M
, P
, GHcop
, HRcop
, rhobevCOP
1 2 |
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