A Note on Multiple Independence Under Multivariate Normal Linear Models

Lattice Models for Conditional Independence in a Multivariate Normal Distribution

A note on nonsymmetric independence models

In probability and statistics graphs are used to describe conditional independence structures (or analogously dependence/association structures) among the variables. Actually, there are three main classic approaches based on undirected graphs [10], directed acyclic graphs [11], or chain graphs [12]. These graphical structures obey graphoid properties (i.e. symmetry, decomposition, weak union, c...

Multivariate linear mixed models for multiple outcomes.

We propose a multivariate linear mixed (MLMM) for the analysis of multiple outcomes, which generalizes the latent variable model of Sammel and Ryan. The proposed model assumes a flexible correlation structure among the multiple outcomes, and allows a global test of the impact of exposure across outcomes. In contrast to the Sammel-Ryan model, the MLMM separates the mean and correlation parameter...

On Linear Independence of a Certain Multivariate Infinite Product

Likelihood Based Finite Sample Inference for Singly Imputed Synthetic Data Under the Multivariate Normal and Multiple Linear Regression Models

In this paper we develop likelihood-based finite sample inference based on singly imputed partially synthetic data, when the original data follow either a multivariate normal or a multiple linear regression model. We assume that the synthetic data are generated by using the plug-in sampling method, where unknown parameters in the data model are set equal to observed values of their point estima...