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MLE for sNiW distributed observations

Source: R/MLE_sNiW.R
MLE_sNiW.Rd

Maximum likelihood estimation of Normal inverse Wishart distributed observations

Usage

MLE_sNiW(xi_list, psi_list, S_list, doPlot = TRUE)

Arguments

xi_list

a list of length N whose elements are observed vectors of length d of the mean parameters xi.

psi_list

a list of length N whose elements are observed vectors of length d of the skew parameters psi.

S_list

a list of length N whose elements are observed variance-covariance matrices of dimension d x d.

doPlot

a logical flag indicating whether the algorithm progression should be plotted. Default is TRUE.

Author

Boris Hejblum, Chariff Alkhassim

Examples

hyperG0 <- list()
hyperG0$b_xi <- c(0.3, -1.5)
hyperG0$b_psi <- c(0, 0)
hyperG0$kappa <- 0.001
hyperG0$D_xi <- 100
hyperG0$D_psi <- 100
hyperG0$nu <- 35
hyperG0$lambda <- diag(c(0.25,0.35))

xi_list <- list()
psi_list <- list()
S_list <- list()
for(k in 1:1000){
 NNiW <- rNNiW(hyperG0, diagVar=FALSE)
 xi_list[[k]] <- NNiW[["xi"]]
 psi_list[[k]] <- NNiW[["psi"]]
 S_list[[k]] <- NNiW[["S"]]
}

mle <- MLE_sNiW(xi_list, psi_list, S_list)
mle
#> $U_xi
#> [1]  0.3089257 -1.5028864
#> 
#> $U_psi
#> [1] -0.01887220 -0.02505573
#> 
#> $U_B
#>              [,1]         [,2]
#> [1,] 0.0099139800 0.0001949264
#> [2,] 0.0001949264 0.0096225162
#> 
#> $U_df
#> [1] 36.01101
#> 
#> $U_Sigma
#>             [,1]        [,2]
#> [1,]  0.25686401 -0.00211768
#> [2,] -0.00211768  0.35839375
#>