Lattice Models for Conditional Independence in a Multivariate Normal Distribution

Lattice Models for Conditional Independence in a Multivariate Normal Distribution

Normal Linear Models with Lattice Conditional Independence Restrictions

Michael D. Perlman-It is shown that each multivariate normal model determined by lattice conditional independence (LeI) restrictions on the covariances may be extended in a, natural way to a normal linear model with corresponding lattice restrictions on the means. For these extended models it remains true that the likelihood function (LF) and parameter space (PS) can be factored into the produc...

Screening for a Multivariate Mixture Normal Distribution

Semi-Parametric Estimation for Conditional Independence Multivariate Finite Mixture Models

Abstract: The conditional independence assumption for nonparametric multivariate finite mixture models, a weaker form of the well-known conditional independence assumption for random effects models for longitudinal data, is the subject of an increasing number of theoretical and algorithmic developments in the statistical literature. After presenting a survey of this literature, including an in-...

Estimation for Conditional Independence Multivariate Finite Mixture Models

The conditional independence assumption for nonparametric multivariate finite mixture models may be considered to be a weaker form of the well-known conditional independence assumption for random effects models for longitudinal data. After summarizing important recent identifiability results, this article describes and extends an algorithm for estimation of the parameters in these models. The a...