Mixed-type multivariate response regression with covariance estimation.

ABSTRACT: We propose a new method for multivariate response regression and covariance estimation when elements of the response vector are of mixed types, for example some continuous and some discrete. Our method is based on a model which assumes the observable mixed-type response vector is connected to a latent multivariate normal response linear regression through a link function. We explore the properties of this model and show its parameters are identifiable under reasonable conditions. We impose no parametric restrictions on the covariance of the latent normal other than positive definiteness, thereby avoiding assumptions about unobservable variables which can be difficult to verify in practice. To accommodate this generality, we propose a novel algorithm for approximate maximum likelihood estim

SUBMITTER: Ekvall KO 

PROVIDER: S-EPMC9313904 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature