Examination of a Simple Errors-in-Variables Model: A Demonstration of Marginal Maximum Likelihood

A simple errors-in-variables regression model is given in this article for illus-trating the method of marginal maximum likelihood (MML). Given suitable esti-mates of reliability, error variables, as nuisance variables, can be integrated out of likelihood equations. Given the closed form expression of the resulting mar-ginal likelihood, the effects of error can be more clearly demonstrated. Deriva-tions are given in detail to provide a detailed example of the marginalization strategy, and to prepare students for understanding more advanced applications of MML