llbb10 {mlCopulaSelection} | R Documentation |
Calculate the log-likelihood for the BB10 density.
llbb10(param, u, v)
param |
bidimensional vector with parameters c(theta ,delta ) (0<theta <1 and 0<delta ) |
u |
vector with the first coordenate of the bivariate data |
v |
vector with the second coordenate of the bivariate data (same size asu ) |
(u,v) margins must have Uniform(0,1) marginal distribution
BB10's log-likelihood function for the sample
Jesus Garcia, IMECC-UNICAMP and Veronica Gonzalez-Lopez, IMECC-UNICAMP
Joe, H., (1997). Multivariate Models and Dependence Concepts. Monogra. Stat. Appl. Probab. 73, London: Chapman and Hall.
# 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<-llbb10(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(dcbb10(param[1],param[2],u[i],v[i]));if(is.nan(s)) {break}; } if(is.finite(s)) {res<-s} else {res<- -10**(64)} }