Symmetry and lattice 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

A Poisson-multivariate normal hierarchical model for measuring microbial conditional independence networks from metagenomic count data

1 Department of Statistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA 2 Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA 3 Howard Hughes Medical Institute, University of North Carolina, Chapel Hill, NC, 27599, USA 4 Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, NC, 27599, USA 5 Departme...

Multivariate mixed normal conditional heteroskedasticity

This paper proposes a new multivariate volatility model where the conditional distribution of a vector time series is given by a mixture of multivariate normal distributions. Each of these distributions is allowed to have a time-varying covariance matrix. The process can be globally covariance-stationary even though some components are not covariance-stationary. Some theoretical properties of t...