Quasi-Linear Bayes Estimation in Stratified Finite Populations

Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 1996 · Volume 58 · Issue 1

DOI: 10.1111/j.2517-6161.1996.tb02082.x

Abstract

In this paper a Bayesian least squares approximation is proposed for descriptive inference in a finite population when a categorical auxiliary variable is known. A hierarchical model II analysis of variance is assumed. The solution consists of a projection on the vector of group totals and on the between and within sums of squares. The approximation can be seen as a normal approximation of the joint distribution of the statistic and of the parameter of interest, conditional on two discrete variables: these denote attribution to one group and selection in the sample. The main consequence of the conditioning is that conjectures about non-normality can be taken into account. The solution is compared with others that have been published.

Keywords

analysis of variance, bayesian least squares approximations, finite populations, hierarchical models, model-based inference

Enable JavaScript for citation tools and additional paper interactions.