Inverse Gaussian model for small area estimation via Gibbs sampling

Abstract

We present a Bayesian method for estimating small area parameters under an inverse Gaussian model. The method is extended to estimate small area parameters for finite populations. The Gibbs sampler is proposed as a mechanism for implementing the Bayesian paradigm. We illustrate the method by application to household income survey data, comparing it against the usual lognormal model for positively skewed data.


Key words/phrases: Finite population sampling, hierarchical Bayesian inference, lognormal model, MCMC integration, shrinkage estimates


SINET: Ethiopian Journal of Science Vol. 28 (1) 2005: 1–14