Sibuya {copula} | R Documentation

## Sibuya Distribution - Sampling and Probabilities

### Description

The Sibuya distribution Sib(alpha) can be defined by its Laplace transform

1-(1-exp(-t))^alpha, t in [0,Inf),

its distribution function

F(k)=1-(-1)^k * choose(alpha-1,k)=1-1/(k * beta(k, 1-alpha)), k in N,

(where beta denotes the beta function) or its probability mass function

p_k = choose(alpha,k)*(-1)^(k-1), k in N,

where alpha in (0,1].

`pSibuya` evaluates the distribution function.

`dSibuya` evaluates the probability mass function.

`rSibuya` generates random variates from 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] ~ Sib(alpha).

This probability mass function can be shown (see Hofert (2010, pp. 99)) to be

sum(j=1..n) choose(n,j) * choose(j*alpha,k) * (-1)^(k-j), k >= n.

### 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.ctrl = 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 <= x), otherwise, P(X > x).

`method`

character string specifying which computational method is to be applied. Implemented are:

`"log"`

  evaluates the logarithm of the sum

  sum(j=1..n) choose(n,j) * choose(j*alpha,x) * (-1)^(x-j)

  in a numerically stable way;

`"direct"`

  directly evaluates the sum;

`"Rmpfr*"`

  are as `method="direct"` but use high-precision arithmetic; `"Rmpfr"` and `"Rmpfr0"` return `double`s whereas `"RmpfrM"` and `"Rmpfr0M"` give `mpfr` high-precision numbers. Whereas `"Rmpfr"` and `"RmpfrM"` each adapt to high enough precision, the `"Rmpfr0*"` ones do not adapt.
  For all `"Rmpfr*"` methods, `alpha` can be set to a `mpfr` number of specified precision and this will determine the precision of all parts of the internal computations.

`"diff"`

  interprets the sum as a forward difference and computes it via `diff`;

`"exp.log"`

  is as `method="log"` but without numerically stable evaluation (not recommended, use with care).

`mpfr.ctrl` | 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. `verbose`: determining if and how much is printed.

### Details

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 >= 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).

### Value

rSibuya:

  A vector of positive `integer`s of length `n` containing the generated random variates.

dSibuya, pSibuya:

  a vector of probabilities of the same length as `x`.

dsumSibuya:

  a vector of probabilities, positive if and only if `x >= n` and of the same length as `x` (or `n` if that is longer).

### References

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.

### 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)

[Package copula version 0.999-15 Index]