B0542
Title: Multivariate conditional transformation models
Authors: Thomas Kneib - University of Goettingen (Germany) [presenting]
Abstract: 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 flexible 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.
