SYNOPTIC ABSTRACT
We derive the asymptotic properties for the eigenvalue and eigenvector of a covariance matrix in the context of two-step monotone incomplete data drawn from , which is a multivariate normal population with mean
and covariance matrix
. Our data consist of n observations of all p + q variables and an additional N – n observations of the first p variables; all observations are mutually independent. We use a maximum likelihood estimator (MLE) and an unbiased estimator (UBE) for a covariance matrix
. Furthermore, we correct for bias of the eigenvalue and eigenvector by evaluating asymptotic expectations. Finally, we investigate the accuracy of our results using numerical simulations.