title: Graphical Models for Ordinal Data.
abstract: A graphical model for ordinal variables is considered, where it is assumed that the data are generated by discretizing the marginal distributions of a latent multivariate Gaussian distribution. The relationships between these ordinal variables are then described by the underlying Gaussian graphical model and can be inferred by estimating the corresponding concentration matrix. Direct estimation of the model is computationally expensive, but an approximate EM-like algorithm is developed to provide an accurate estimate of the parameters at a fraction of the computational cost. Numerical evidence based on simulation studies shows the strong performance of the algorithm, which is also illustrated on data sets on movie ratings and an educational survey.
authors.affiliation: Department of Biostatistics, Harvard University.
authors.forename: Jian
authors.initials: J
authors.lastname: Guo
authors.affiliation: Department of Statistics, University of Michigan, Ann Arbor.
authors.forename: Elizaveta
authors.initials: E
authors.lastname: Levina
authors.affiliation: Department of Statistics, University of Michigan, Ann Arbor.
authors.forename: George
authors.initials: G
authors.lastname: Michailidis
authors.affiliation: Department of Statistics, University of Michigan, Ann Arbor.
authors.forename: Ji
authors.initials: J
authors.lastname: Zhu
keywords: Graphical model; lasso; ordinal variable; probit model