  A difficulty in MSE estimation occurs because we do not specify a full
distribution for the survey weights. This obfuscates the use of fully
parametric bootstrap procedures. To overcome this challenge, we develop a novel
MSE estimator. We estimate the leading term in the MSE, which is the MSE of the
best predictor (constructed with the true parameters), using the same simulated
samples used to construct the basic predictor. We then exploit the asymptotic
normal distribution of the parameter estimators to estimate the second term in
the MSE, which reflects variability in the estimated parameters. We incorporate
a correction for the bias of the estimator of the leading term without the use
of computationally intensive double-bootstrap procedures. We further develop
calibrated prediction intervals that rely less on normal theory than standard
prediction intervals. We empirically demonstrate the validity of the proposed
procedures through extensive simulation studies. We apply the methods to
predict several functions of sheet and rill erosion for Iowa counties using
data from a complex agricultural survey.
