cf_TriangularSymmetric: Characteristic function of of a linear combination of...

In gajdosandrej/CharFunToolR: Numerical Computation Cumulative Distribution Function and Probability Density Function from Characteristic Function

Characteristic function of of a linear combination of independent zero-mean symmetric TRIANGULAR random variables

Description

cf_TriangularSymmetric(t, coef, niid) evaluates the characteristic function of a linear combination (resp. convolution) of independent zero-mean symmetric TRIANGULAR random variables defined on the interval (-1,1).

That is, cf_TriangularSymmetric evaluates the characteristic function cf(t) of Y = sum_{i=1}^N coef_i * X_i, where X_i ~ TriangularSymmetric are independent uniformly distributed RVs defined on (-1,1), for all i = 1,...,N.

The characteristic function of X ~ TriangularSymmetric is defined by

cf(t) = cf_TriangularSymmetric(t) = (2-2*cos(t))/t^2.

Usage

cf_TriangularSymmetric(t, coef, niid)

Arguments

t	vector or array of real values, where the CF is evaluated.
coef	vector of the coefficients of the linear combination of the zero-mean symmetric TRIANGULAR random variables. If coef is scalar, it is assumed that all coefficients are equal. If empty, default value is coef = 1.
niid	scalar convolution coeficient niid, such that Z = Y + ... + Y is sum of niid iid random variables Y, where each Y = sum_{i=1}^N coef(i) * log(X_i) is independently and identically distributed random variable. If empty, default value is niid = 1.

Value

Characteristic function cf(t) of a linear combination of independent zero-mean symmetric TRIANGULAR random variables.

Note

Ver.: 16-Sep-2018 18:40:04 (consistent with Matlab CharFunTool v1.3.0, 02-Jun-2017 12:08:24).

See Also

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

Other Continuous Probability Distribution: cfS_Arcsine(), cfS_Beta(), cfS_Gaussian(), cfS_Laplace(), cfS_Rectangular(), cfS_Student(), cfS_TSP(), cfS_Trapezoidal(), cfS_Triangular(), cfS_Wigner(), cfX_ChiSquare(), cfX_Exponential(), cfX_FisherSnedecor(), cfX_Gamma(), cfX_InverseGamma(), cfX_LogNormal(), cf_ArcsineSymmetric(), cf_BetaNC(), cf_BetaSymmetric(), cf_Beta(), cf_ChiSquare(), cf_Exponential(), cf_FisherSnedecorNC(), cf_FisherSnedecor(), cf_Gamma(), cf_InverseGamma(), cf_Laplace(), cf_LogRV_BetaNC(), cf_LogRV_Beta(), cf_LogRV_ChiSquareNC(), cf_LogRV_ChiSquare(), cf_LogRV_FisherSnedecorNC(), cf_LogRV_FisherSnedecor(), cf_LogRV_MeansRatioW(), cf_LogRV_MeansRatio(), cf_LogRV_WilksLambdaNC(), cf_LogRV_WilksLambda(), cf_Normal(), cf_RectangularSymmetric(), cf_Student(), cf_TSPSymmetric(), cf_TrapezoidalSymmetric(), cf_vonMises()

Other Symmetric Probability Distribution

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.: cfS_Triangular()

Examples

## EXAMPLE 1
# CF of the Triangular distribution on (-1,1)
t <- seq(from = -50,
         to = 50,
         length.out = 201)
plotReIm(function(t)
        cf_TriangularSymmetric(t),
        t,
        title = "CF of the Triangular distribution on (-1,1)")

## EXAMPLE 2
# CF of a weighted linear combination of independent Triangular RVs
t <- seq(from = -20,
         to = 20,
         length.out = 201)
coef <- c(1, 2, 3, 4, 5) / 15
plotReIm(function(t)
        cf_TriangularSymmetric(t, coef),
        t,
        title = "CF of a weighted linear combination of Triangular RVs")

## EXAMPLE 3
# PDF/CDF of a weighted linear combination of independent Triangular RVs
coef <- c(1, 2, 3, 4, 5) / 15
cf <- function(t)
        cf_TriangularSymmetric(t, coef)
x <- seq(from = -1,
         to = 1,
         length.out = 201)
prob <- c(0.9, 0.95, 0.99)
options <- list()
options$N <- 2 ^ 12
options$xMin <- -1
options$xMax <- 1
result <- cf2DistGP(cf, x, prob, options)

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