Pattern mixture models for multivariate normal data: a simulation study.
Sonderforschungsbereich 386, Discussion Paper 191
Little&Wand (1996) introduced the pattern mixture model for wave nonresponse as a special case of multivariate normal longitudinal data with fixed covariate matrix. This paper was the theoretical foundation and the induce to investigate the pattern mixture model compared with complete case analysis by means of simulations. The main point of interest was the mean square error of the estimated model parameters and the efficiency of the estimations. To estimate the variance of the model parameters we examine the Jackknife method. Parameter estimates by the pattern mixture model are very satisfying under ignorable mechanism but they have to be scanned carefully under nonignorable mechanism. The Jackknife method seems to be, with restrictions, a good estimator for the variance of the model parameters.