Estimation of the Correlation Coefficient from a Broken Random Sample

Abstract

Inference about the correlation coefficient $\rho$ in a bivariate normal distribution is considered when observations from the distribution are available only in the form of a broken random sample. In other words, a random sample of $n$ pairs is drawn from the distribution but the observed data are only the first components of the $n$ pairs and, separately, some unknown permutation of the second components of the $n$ pairs. Under these conditions, the estimation of $\rho$ is, as Samuel Johnson put it, "like a dog's walking on his hinder legs. It is not done well; but you are surprised to find it done at all." We study the maximum likelihood estimation of $\rho$ and present some effective procedures for estimating the sign of $\rho$.