View source: R/cfN_Logarithmic.R
cfN_Logarithmic | R Documentation |
cfN_Logarithmic(t, p, cfX)
evaluates the characteristic function cf(t)
of the
Logarithmic distribution defined on non-negative integers n =0, 1, \ldots
,
with the parameters p
(success probability p
in [0,1]
), i.e.
cfN_Logarithmic(t, p) = log(1 - p * exp(1i*t)) / log(1 - p).
For more details see [4].
cfN_Logarithmic(t,p,cfX)
evaluates the compound characteristic function
cf(t) = cfN_Logarithmic(-1i*log(cfX(t)), 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)
.
cfN_Logarithmic(t, p = 1, cfX)
t |
numerical values (number, vector...). |
p |
success probability, |
cfX |
function. |
Characteristic function cf(t)
of the Logarithmic distribution.
Ver.: 16-Sep-2018 19:01:18 (consistent with Matlab CharFunTool v1.3.0, 15-Nov-2016 13:36:26).
[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.
For more details see WIKIPEDIA: https://en.wikipedia.org/wiki/Logarithmic_distribution.
Other Discrete Probability Distribution:
cfN_Binomial()
,
cfN_Delaporte()
,
cfN_GeneralizedPoisson()
,
cfN_Geometric()
,
cfN_NegativeBinomial()
,
cfN_Poisson()
,
cfN_PolyaEggenberger()
,
cfN_Quinkert()
,
cfN_Waring()
## EXAMPLE1
# CF of the Logarithmic distribution with the parameter p = 0.5
p <- 0.5
t <- seq(-10, 10, length.out = 501)
plotReIm(function(t)
cfN_Logarithmic(t, p),
t,
title = "CF of the Logarithmic distribution with the parameter p = 0.5")
## EXAMPLE2
# CF of the compound Logarithmic-Exponential distribution
p <- 0.5
lambda <- 5
cfX <- function(t)
cfX_Exponential(t, lambda)
t <- seq(-10, 10, length.out = 501)
plotReIm(function(t)
cfN_Logarithmic(t, p, cfX),
t,
title = "CF of the compound Logarithmic-Exponential distribution")
## EXAMPLE3
# PDF/CDF of the compound Logarithmic-Exponential distribution
p <- 0.5
lambda <- 5
cfX <- function(t)
cfX_Exponential(t, lambda)
cf <- function(t)
cfN_Logarithmic(t, p, cfX)
x <- seq(0, 3, length.out = 101)
prob <- c(0.9, 0.95, 0.99)
options <- list()
options$isCompound <- TRUE
result <- cf2DistGP(cf, x, prob, options)
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