JOcopB5 | R Documentation |
The Joe/B5 copula (Joe, 2014, p. 170) is
\mathbf{C}_{\Theta}(u,v) = \mathbf{B5}(u,v) = 1 - \bigl((1-u)^\Theta + (1-v)^\Theta - (1-u)^\Theta (1-v)^\Theta\bigr)\mbox{,}
where \Theta \in [1,\infty)
.
The copula as \Theta \rightarrow \infty
limits to the comonotonicity coupla (\mathbf{M}(u,v)
and M
), as \Theta \rightarrow 1^{+}
limits to independence copula (\mathbf{\Pi}(u,v)
; P
). Finally, the parameter \Theta
is readily computed from a Kendall Tau (tauCOP
) by
\tau_\mathbf{C} = 1 + \frac{2}{2-\Theta}\bigl(\psi(2) - \psi(1 + 2/\Theta)\bigr)\mbox{,}
where \psi
is the digamma()
function and as \Theta \rightarrow 2
then
\tau_\mathbf{C}(\Theta \rightarrow 2) = 1 - \psi'(2)
where \psi'
is the trigamma()
function.
JOcopB5(u, v, para=NULL, tau=NULL, ...)
u |
Nonexceedance probability |
v |
Nonexceedance probability |
para |
A vector (single element) of parameters—the |
tau |
Optional Kendall Tau; and |
... |
Additional arguments to pass. |
Value(s) for the copula are returned. Otherwise if tau
is given, then the \Theta
is computed and a list
having
para |
The parameter |
tau |
Kendall Tau. |
and if para=NULL
and tau=NULL
, then the values within u
and v
are used to compute Kendall Tau and then compute the parameter, and these are returned in the aforementioned list.
W.H. Asquith
Joe, H., 2014, Dependence modeling with copulas: Boca Raton, CRC Press, 462 p.
M
, P
# Upper tail dependency of Theta = pi --> 2 - 2^(1/pi) = 0.753131 (Joe, 2014, p. 171).
taildepCOP(cop=JOcopB5, para=pi)$lambdaU # 0.75313
# Blomqvist Beta of Theta = pi (Joe, 2014, p. 171).
blomCOP(cop=JOcopB5, para=pi) # 0.5521328
3 - 4*(2*(1/2)^pi - (1/4)^pi)^(1/pi) # 0.5521328
## Not run:
# A test near the limiting Theta for trigamma()
UV <- simCOP(cop=JOcopB5, para=2, n=10000)
para <- JOcopB5(UV[,1], UV[,2])$para
message("Tau difference ", round(2-para, digits=2), " is small.") #
## End(Not run)
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