Empirical Bayes predictors of small area parameters of interest are often obtained under a linear mixed model for continuous response data or a generalized linear mixed model for binary responses or count data. However, estimation of the unconditional mean squared error of prediction is...

In this paper based on a basic area level model we obtain second-order accurate approximations to the mean squared error of model-based small area estimators, using the Fay & Herriot (1979) iterative method of estimating the model variance based on weighted residual sum of squares. We also...

Most methods for analysing cluster-correlated biological data implicitly assume the ignorability of cluster sizes. When this assumption fails, the resulting inferences may be asymptotically invalid. Hoffman et al. (2001) proposed a simple but computationally intensive method, based on a large...

A simple transformation method, proposed by Fuller and Battese (1973), for making inferences from one and two-fold nested error regression models with equal error variances under two and three-stage cluster sampling is extended here to the more realistic case of unequal error variances. The...

Powers of a generalized least squares F test and an adjusted F test are studied under regression models with nested error structure.

A linear model with random effects, [mu]i, and random error variances, [sigma]i, is considered. The linear Bayes estimator or the best linear unbiased predictor (BLUP) of [mu]i is first obtained, and then the unknown parameters in the model are estimated to arrive at the empirical linear Bayes...

Combining information from two or more independent surveys is a problem frequently encountered in survey sampling. We consider the case of two independent surveys, where a large sample from survey 1 collects only auxiliary information and a much smaller sample from survey 2 provides information...