# R/eblup.mse.f.c3.star.R In JoSAE: Unit-Level and Area-Level Small Area Estimation

#### Documented in eblup.mse.f.c3.star

```    #the star-third componetnt
eblup.mse.f.c3.star <- function(lme.obj, asympt.var.covar#=lme.obj\$apVar
, n.i, mean.resid.i,...){
var.v <- as.numeric(VarCorr(lme.obj)[,1])[1]
var.e <- as.numeric(VarCorr(lme.obj)[,1])[2]
#approx covariances - is that right?
## V.bar.vv <- lme.obj\$apVar[1,1]
## V.bar.ee <- lme.obj\$apVar[2,2]
## V.bar.ve <- lme.obj\$apVar[1,2]
#inverted apprx var-cov matrix
inv.var.covar <- solve(asympt.var.covar)
V.bar.vv <- inv.var.covar[1,1]
V.bar.ee <- inv.var.covar[2,2]
V.bar.ve <- inv.var.covar[1,2]
#7.2.23 this is how Rao suggests it - correct?
h <- var.e^2 * V.bar.vv + var.v^2 * V.bar.ee - 2 * var.e * var.v * V.bar.ve
#is it not correct to multiply the vars with the asympt vars?
#h <- var.e^2 * V.bar.ee + var.v^2 * V.bar.vv - 2 * var.e * var.v * V.bar.ve
#7.2.32
res <- n.i^-2 * (var.v + var.e/n.i)^-4 * h * mean.resid.i^2#what is correct? see 7.2.22
#class(res) <- "eblup.mse.f"
return(res)
}
```

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JoSAE documentation built on May 6, 2018, 5:04 p.m.