An Expectation-Maximization (EM) algorithm in a maximum likelihood framework is developed to estimate finite mixtures of multivariate regression and simultaneous equation models with multiple endogenous variables. A dataset with cross-sectional observations for a diverse sample of businesses illustrates the semiparametric approach. (SLD)

The Gibbs sampling algorithms presented by M. H. Seltzer (1993) are fully generalized to a broad range of settings in which vectors of random regression parameters in the hierarchical model are assumed multivariate normally or multivariate "t" distributed across groups. The use of a fully Bayesian approach is discussed. (SLD)

A general model for two-level multivariate data, with responses possibly missing at random, is described. The model combines regressions on fixed explanatory variables with structured residual covariance matrices. The likelihood function is reduced to a form enabling computational methods for estimating the model to be devised. (Author)

Several methods for dealing with incomplete multivariate data and ways to examine the effectiveness of these methods are discussed. It is concluded that pairwise and listwise deletions are among the least effective methods in terms of approximating the results, whereas estimates based on correlational procedures generally produce the most accurate…

This paper provides a method for analyzing data consisting of event sequences and covariate observations associated with Markov chains. The objective is to use the covariate data to explain differences between individuals in the transition probability matrices characterizing their sequential data. (TJH)

This study analyzed issues related to estimating indirect medical education costs specific to pediatric discharges. The Children's Hospital Graduate Medical Education (CHGNE) program was established to support graduate medical education in children's hospitals. This provision authorizes payments for both direct and indirect medical education…