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ForestFit (version 2.4.3)

djsbb: Computing the probability density function of bivariate Johnson's SB (JSBB) distribution

Description

Computes the probability density function of the 9-parameter JSBB distibution given by $$ f_{Y_{1},Y_{2}}\bigl(y_1,y_2\big \vert\Theta\bigr) = f_{Y_1, Y_2}(y_1, y_2) =\frac{\delta_1\delta_2\lambda_1\lambda_2\exp\Bigl\{\frac{-z^{2}_{1}-z^{2}_{2} +2\rho z_{1}z_{2}}{2(1-\rho^2)}\Bigr\}}{2\pi \sqrt{1-\rho^2}\bigl(y_1-\xi_1\bigr)\bigl(y_2-\xi_2\bigr)\bigl(\lambda_1+\xi_1-y_1\bigr)\bigl(\lambda_2+\xi_2-y_2\bigr)}, $$ where $$ z_{i}=\delta_i \log \Bigl(\frac{y_{i}-{\xi}_i}{{\xi}_i+{\lambda}_i-y_{i}}\Bigr)+\gamma_{i}, $$ for \(i=1,2\). The parameter space of SBB distribution is \(\Theta=({\bf{\delta}},{\bf{\gamma}},{\bf{\lambda}},{\bf{\xi}}, \rho)^{\top}\) in which \({\bf{\delta}}=(\delta_1,\delta_2)^{\top}\), \({\bf{\gamma}}=(\gamma_1,\gamma_2, \rho)^{\top}\), \({\bf{\lambda}}=(\lambda_1,\lambda_2)^{\top}\), and \({\bf{\xi}}=(\xi_1,\xi_2)^{\top}\). The supports of marginals are \(\xi_1<y_1<\lambda_1+\xi_1\) and \(\xi_2<y_2<\lambda_2+\xi_2\). The support of the parameter space is \(\delta_1>0,\delta_2>0,-\infty<\gamma_1<+\infty,-\infty<\gamma_2<+\infty, \lambda_1>0,\lambda_2>0, -\infty<\xi_1<+\infty, -\infty<\xi_2<+\infty\) and \(-1<\rho<+1\).

Usage

djsbb(data, param, log = FALSE)

Value

A vector of length n, giving the density function of JSBB distribution.

Arguments

data

Vector of observations.

param

Vector of the parameters \({\bf{\delta}}\), \({\bf{\gamma}}\), \({\bf{\lambda}}\), \({\bf{\xi}}\), \(\rho\).

log

If TRUE, then log of density function is returned.

Author

Mahdi Teimouri

Examples

Run this code
Delta <- c(2.5, 3)
Gamma <- c(2, 1)
Lambda <- c(1, 3)
Xi <- c(0, 2)
rho <- -0.5
param <- c(Delta[1], Gamma[1], Lambda[1], Xi[1], Delta[2], Gamma[2], Lambda[2], Xi[2], rho)
data <- rjsbb(20, param)
djsbb(data, param, log = FALSE)

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