Correspondence analysis with incomplete paired data using Bayesian imputation

Abstract

In this paper we consider the analysis of incomplete tables using correspondence analysis. 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. We 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.