Sibuya | R Documentation |
The Sibuya distribution \mathrm{Sib}(\alpha)
can be
defined by its Laplace transform
1-(1-\exp(-t))^\alpha,\ t\in[0,\infty),
its distribution function
F(k)=1-(-1)^k{\alpha-1\choose k}=1-\frac{1}{kB(k,1-\alpha)},\
k\in\mathbf{N}
(where B
denotes the beta function) or its probability
mass function
p_k={\alpha\choose k}(-1)^{k-1},\ k\in\mathbf{N},
where \alpha\in(0,1]
.
pSibuya
evaluates the distribution function.
dSibuya
evaluates the probability mass function.
rSibuya
generates random variates from
\mathrm{Sib}(\alpha)
with
the algorithm described in Hofert (2011), Proposition 3.2.
dsumSibuya
gives the probability mass function of the
n
-fold convolution of Sibuya variables, that is, the sum of n
independent Sibuya random variables,
S = \sum_{i=1}^n X_i
, where
X_i \sim \mathrm{Sib}(\alpha)
.
This probability mass function can be shown (see Hofert (2010, pp. 99)) to be
\sum_{j=1}^n{n\choose j}{j\alpha\choose k} (-1)^{k-j},\
k\in\{n,n+1,\dots\}.
rSibuya(n, alpha)
dSibuya(x, alpha, log=FALSE)
pSibuya(x, alpha, lower.tail=TRUE, log.p=FALSE)
dsumSibuya(x, n, alpha,
method=c("log", "direct", "diff", "exp.log",
"Rmpfr", "Rmpfr0", "RmpfrM", "Rmpfr0M"),
mpfr.ctrl = list(minPrec = 21, fac = 1.25, verbose=TRUE),
log=FALSE)
n |
for |
alpha |
parameter in |
x |
vector of |
log , log.p |
|
lower.tail |
|
method |
character string specifying which computational method is to be applied. Implemented are:
|
mpfr.ctrl |
for |
The Sibuya distribution has no finite moments, that is, specifically infinite mean and variance.
For documentation and didactical purposes, rSibuyaR
is a pure-R
implementation of rSibuya
, of course slower than rSibuya
as the latter is implemented in C.
Note that the sum to evaluate for dsumSibuya
is numerically
highly challenging, even already for small
\alpha
values (for example, n \ge 10
),
and therefore should be used with care. It may require high-precision
arithmetic which can be accessed with method="Rmpfr"
(and the
Rmpfr package).
A vector of positive integer
s of
length n
containing the generated random variates.
a vector of
probabilities of the same length as x
.
a vector of probabilities, positive if and only if
x >= n
and of the same length as x
(or n
if
that is longer).
Hofert, M. (2010). Sampling Nested Archimedean Copulas with Applications to CDO Pricing. Südwestdeutscher Verlag fuer Hochschulschriften AG & Co. KG.
Hofert, M. (2011). Efficiently sampling nested Archimedean copulas. Computational Statistics & Data Analysis 55, 57–70.
rFJoe
and rF01Joe
(where rSibuya
is
applied).
## Sample n random variates from a Sibuya(alpha) distribution and plot a
## histogram
n <- 1000
alpha <- .4
X <- rSibuya(n, alpha)
hist(log(X), prob=TRUE); lines(density(log(X)), col=2, lwd=2)
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