llbb1 {mlCopulaSelection}R Documentation

BB1's log-likelihood function

Description

Calculate the log-likelihood for the BB1 density.

Usage

llbb1(param, u, v)

Arguments

param bidimensional vector with parameters c(theta,delta) (0<theta and 1<delta)
u vector with the first coordenate of the bivariate data
v vector with the second coordenate of the bivariate data (same size asu)

Details

(u,v) margins must have Uniform(0,1) marginal distribution

Value

BB1's log-likelihood function for the sample

Author(s)

Jesus Garcia, IMECC-UNICAMP and Veronica Gonzalez-Lopez, IMECC-UNICAMP

References

Joe, H., (1997). Multivariate Models and Dependence Concepts. Monogra. Stat. Appl. Probab. 73, London: Chapman and Hall.

Examples

# The data:
u <- c( 0.43, 0.1, 0.2, 0.33, 0.24, 0.29, 0.14, 0.4, 0.39, 0.8, 0.63, 0.16, 0.24, 0.14,0.71, 0.39, 0.48, 0.29, 0.38, 0.37)
v <- c(0.01, 0.26, 0.2, 0.36, 0.34, 0.43, 0.27, 0.61, 0.08, 0.25, 0.72, 0.15, 0.14, 0.12, 0.74, 0.18, 0.58, 0.15, 0.34, 0.13)
# The log-likelihood 
r<-llbb1(c(0.5,1.5),u,v)

## The function is currently defined as
function(param,u,v)
{
n<-sum(u>=-1)
s<-0.
for(i in 1:n) { s<-s+log( dcbb1(param[1],param[2],u[i],v[i]) );if(is.nan(s)) {break};}
if(is.finite(s)) {res<-s} else {res<- -10**(64)}
  }

[Package mlCopulaSelection version 1.3 Index]