copula (version 0.999-7)

Sibuya: Sibuya Distribution - Sampling and Probabilities

Description

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 $$\sum_{j=1}^n{n\choose j}{j\alpha\choose k} (-1)^{k-j},\ k\in{n,n+1,\dots}.$$

Usage

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 = list(minPrec = 21, fac = 1.25, verbose=TRUE),
           log=FALSE)

Arguments

n

for rSibuya: sample size, that is, length of the resulting vector of random variates. for dsumSibuya: the number $n$ of summands.

alpha

parameter in $(0,1]$.

x

vector of integer values (quantiles) $x$ at which to compute the probability mass or cumulative probability.

log, log.p

logical; if TRUE, probabilities p are given as log(p).

lower.tail

logical; if TRUE (the default), probabilities are $P(X \le x)$, otherwise, $P(X > x)$.

method

character string specifying which computational method is to be applied. Implemented are: [object Object],[object Object],[object Object],[object Object],[object Object]

mpfr

for method = "Rmpfr" or "RmpfrM" only: a list of minPrec: minimal (estimated) precision in bits, fac: factor with which current precision is multiplied if it is not sufficient.

Value

• [object Object],[object Object],[object Object]

Details

The Sibuya distribution has no finite moments, that is, specifically infinite mean and variance.

For documentation and didactical purposes, rSibuyaR is a pure-Rimplementation 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).

References

Hofert, M. (2010). Sampling Nested Archimedean Copulas with Applications to CDO Pricing. Sü{ue}dwestdeutscher Verlag fuer Hochschulschriften AG & Co. KG.

Hofert, M. (2011). Efficiently sampling nested Archimedean copulas. Computational Statistics & Data Analysis 55, 57--70.

See Also

rFJoe and rF01Joe (where rSibuya is applied).

Examples

## 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)