rF01FrankJoe | R Documentation |
rF01Frank
:
Generate a vector of random variates V_{01}\sim F_{01}
with Laplace-Stieltjes transform
\psi_{01}(t;V_0)=
\Bigl(\frac{1-(1-\exp(-t)(1-e^{-\theta_1}))^{\theta_0/\theta_1}}{%
1-e^{-\theta_0}}\Bigr)^{V_0}.
for the given realizations V_0
of Frank's F_0
and the
parameters \theta_0,\theta_1\in(0,\infty)
such that \theta_0\le\theta_1
. This distribution
appears on sampling nested Frank copulas. The parameter rej
is used
to determine the cut-off point of two algorithms that are involved in
sampling F_{01}
. If
\code{rej} < V_0\theta_0(1-e^{-\theta_0})^{V_0-1}
a rejection from F_{01}
of Joe is applied (see
rF01Joe
; the meaning of the parameter approx
is
explained below), otherwise a sum is sampled with a logarithmic
envelope for each summand.
rF01Joe
:
Generate a vector of random variates V_{01}\sim F_{01}
with Laplace-Stieltjes transform
\psi_{01}(t;V_0)=(1-(1-\exp(-t))^\alpha)^{V_0}.
for the given realizations V_0
of Joe's F_0
and
the parameter \alpha\in(0,1]
. This distribution
appears on sampling nested Joe copulas. Here,
\alpha=\theta_0/\theta_1
, where
\theta_0,\theta_1\in[1,\infty)
such
that \theta_0\le\theta_1
. The parameter
approx
denotes the largest number of summands in the
sum-representation of V_{01}
before the asymptotic
V_{01}=V_0^{1/\alpha}S(\alpha,1,\cos^{1/\alpha}(\alpha\pi/2),
\mathbf{1}_{\{\alpha=1\}};1)
is used to sample V_{01}
.
rF01Frank(V0, theta0, theta1, rej, approx)
rF01Joe(V0, alpha, approx)
V0 |
a vector of random variates from |
theta0 , theta1 , alpha |
parameters
|
rej |
parameter value as described above. |
approx |
parameter value as described above. |
A vector of positive integer
s of length n
containing the generated random variates.
Hofert, M. (2011). Efficiently sampling nested Archimedean copulas. Computational Statistics & Data Analysis 55, 57–70.
rFFrank
, rFJoe
, rSibuya
,
and rnacopula
.
rnacopula
## Sample n random variates V0 ~ F0 for Frank and Joe with parameter
## chosen such that Kendall's tau equals 0.2 and plot histogram
n <- 1000
theta0.F <- copFrank@iTau(0.2)
V0.F <- copFrank@V0(n,theta0.F)
hist(log(V0.F), prob=TRUE); lines(density(log(V0.F)), col=2, lwd=2)
theta0.J <- copJoe@iTau(0.2)
V0.J <- copJoe@V0(n,theta0.J)
hist(log(V0.J), prob=TRUE); lines(density(log(V0.J)), col=2, lwd=2)
## Sample corresponding V01 ~ F01 for Frank and Joe and plot histogram
## copFrank@V01 calls rF01Frank(V0, theta0, theta1, rej=1, approx=10000)
## copJoe@V01 calls rF01Joe(V0, alpha, approx=10000)
theta1.F <- copFrank@iTau(0.5)
V01.F <- copFrank@V01(V0.F,theta0.F,theta1.F)
hist(log(V01.F), prob=TRUE); lines(density(log(V01.F)), col=2, lwd=2)
theta1.J <- copJoe@iTau(0.5)
V01.J <- copJoe@V01(V0.J,theta0.J,theta1.J)
hist(log(V01.J), prob=TRUE); lines(density(log(V01.J)), col=2, lwd=2)
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