Finite population prediction under a linear function superpopulation model:a bayesian perspective

Abstract

Exact and approximate Bayesian inference is developed for the prediction problem in finite populations under a linear functional superpopulation model. The models considered are the usual regression models involving two variables, X and Y, where the independent variable X is measured with error. The approach is based on the conditional distribution of Y given X and our predictor is the posterior mean of the quantity of interest (population total and population variance) given the observed data. Empirical investigations about optimal purposive samples and possible model misspecifications based on comparisons with the corresponding models where X is measured without error are also reported.