Correspondence Analysis with Incomplete Data using Bayesian imputation

In this presentation we consider the analysis of incomplete tables using Correspondence Analysis (CA). We focus on a dataset concerning congenital heart disease (Fraser and Hunter 1975), in which the data forms a square table; but only a symmetrized version of the off-diagonal entries was reported. Wer use Markov chain Monte Carlo (MCMC) on a hierarchical Bayes model to estimate the underlying rates, and use Correspondence Analysis to study the relationships in the completed table.