Estimating King’s ecological inference normal model via the EM Algorithm1

Recently, King (1997) introduced a new model for ecological inference (EI), based on a truncated bivariate normal, which he estimates by maximum probability and uses to simulate the predictive densities of the disaggregate data. This paper reviews King's model and its assumption of truncated normality, with the aim to implement maximum probability estimation of his model and disaggregate data prediction in an alternative fashion via the EM Algorithm. In addition, we highlight and discuss important modeling issues related to the chance of non– existence of maximum likelihood estimates, and to the degree that corrections for this non–existence by means of suitably chosen priors are effective. At the end, a Monte Carlo simulation study is run in order to compare the two approaches