cfN_NegativeBinomial: Characteristic function of the Negative-Binomial distribution

View source: R/cfN_NegativeBinomial.R

cfN_NegativeBinomialR Documentation

Characteristic function of the Negative-Binomial distribution

Description

cfN_NegativeBinomial(t, r, p) evaluates the characteristic function cf(t) of the Negative-Binomial distribution with the parameters r (number of failures until the experiment is stopped, r is a natural number) and p (success probability in each experiment, p in [0,1]), i.e.

cfN_NegativeBinomial(t, r, p) = p^r * (1 - (1-p) * e^(1i*t))^(-r).

For more details see [4].

cfN_NegativeBinomial(t, r, p, cfX) evaluates the compound characteristic function

cf(t) = cfN_NegativeBinomial(-1i*log(cfX(t)), r, p),

where cfX is function handle of the characteristic function cfX(t) of a continuous distribution and/or random variable X.

Note that such CF is characteristic function of the compound distribution, i.e. distribution of the random variable Y = X_1 + ... + X_N, where X_i ~ F_X are i.i.d. random variables with common CF cfX(t), and N ~ F_N is independent RV with its CF given by cfN(t).

Usage

cfN_NegativeBinomial(t, r = 10, p = 0.5, cfX)

Arguments

t

vector or array of real values, where the CF is evaluated.

r

number of trials.

p

success probability, 0 \le p \le 1, default value p = 1/2.

cfX

function.

Value

Characteristic function cf(t) of the Negative-Binomial distribution.

Note

Ver.: 16-Sep-2018 19:01:54 (consistent with Matlab CharFunTool v1.3.0, 15-Nov-2016 13:36:26).

References

[1] WITKOVSKY V., WIMMER G., DUBY T. (2016). Computing the aggregate loss distribution based on numerical inversion of the compound empirical characteristic function of frequency and severity. Preprint submitted to Insurance: Mathematics and Economics.

[2] DUBY T., WIMMER G., WITKOVSKY V.(2016). MATLAB toolbox CRM for computing distributions of collective risk models. Preprint submitted to Journal of Statistical Software.

[3] WITKOVSKY V. (2016). Numerical inversion of a characteristic function: An alternative tool to form the probability distribution of output quantity in linear measurement models. Acta IMEKO, 5(3), 32-44.

[4] WIMMER G., ALTMANN G. (1999). Thesaurus of univariate discrete probability distributions. STAMM Verlag GmbH, Essen, Germany. ISBN 3-87773-025-6.

See Also

For more details see WIKIPEDIA: https://en.wikipedia.org/wiki/Negative_binomial_distribution.

Other Discrete Probability Distribution: cfN_Binomial(), cfN_Delaporte(), cfN_GeneralizedPoisson(), cfN_Geometric(), cfN_Logarithmic(), cfN_Poisson(), cfN_PolyaEggenberger(), cfN_Quinkert(), cfN_Waring()

Examples

## EXAMPLE1
# CF of the Negative Binomial distribution with r = 5, p = 0.3
r <- 5
p <- 0.3
t <- seq(-15, 15, length.out = 1001)
plotReIm(function(t)
        cfN_NegativeBinomial(t, r, p), t,
        title = "CF of the Negative Binomial distribution with r = 5, p = 0.3")

## EXAMPLE2
# PDF/CDF of the compound NegativeBinomial-Exponential distribution
r <- 5
p <- 0.3
lambda <- 5
cfX <- function(t)
        cfX_Exponential(t, lambda)
cf <- function(t)
        cfN_NegativeBinomial(t, r, p, cfX)
x <- seq(0, 10, length.out = 101)
prob <- c(0.9, 0.95, 0.99)
options <- list()
options$isCompound <- TRUE
result <- cf2DistGP(cf, x, prob, options)

gajdosandrej/CharFunToolR documentation built on June 3, 2024, 7:46 p.m.