Resumen de Multivariate conditional transformation models

Thomas Kneib, Nadja Klein, Torsten Hothorn

• Regression models describing the joint distribution of multivariate response variables conditional on covariate information have become an important aspect of contemporary regression analysis. However, a limitation of such models is that they often rely on rather simplistic assumptions, e.g. a constant dependence structure that is not allowed to vary with the covariates or the restriction to linear dependence between the responses only. We propose a general framework for multivariate conditional transformation models that overcomes such limitations and describes the full joint distribution in a tractable and interpretable yet exible way. Among the particular merits of the framework are that it can be embedded into likelihood-based inference (including results on asymptotic normality) and allows the dependence structure to vary with the covariates.

  In addition, the framework scales well beyond bivariate response situations.