R: MLE for sNiW distributed observations

MLE_sNiW {NPflow} | R Documentation

## MLE for sNiW distributed observations

### Description

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(s)

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

[Package NPflow version 0.13.3 Index]