Analysis of the linear correlation coefficient using pseudo-likelihoods

Abstract

The problem of the inferential analysis of the linear correlation coefficient of normal bivariate populations is tackled, both from the likelihood and Bayesian viewpoints. In particular it is shown how, using pseudo-likelihood (marginal likelihood function and profile likelihood), hypotheses such asH0:ϱ=ϱ0 andH0:ϱx=ϱy can be verified without prohibitive computation effort. The results of marginal and profile likelihood are compared and it is shown that these two methods are virtually equivalent even for small sample sizes. Furthermore, in suitable conditions, the posterior distribution of the coefficient ϱ can be readily obtained, using the exact form or different approximate formulations of the marginal or profile likelihood. Lastly some possible prior distributions of ϱ are illustrated and some explanatory examples are presented.