Multivariate conditional transformation models

• Autores: Thomas Kneib, Nadja Klein, Torsten Hothorn
• Localización: Proceedings of the 35th International Workshop on Statistical Modelling : July 20-24, 2020 Bilbao, Basque Country, Spain / Itziar Irigoien Garbizu (ed. lit.), Dae-Jin Lee (ed. lit.), Joaquín Martínez Minaya (ed. lit.), María Xosé Rodríguez Álvarez (ed. lit.), 2020, ISBN 978-84-1319-267-3, págs. 131-136
• Idioma: inglés
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• Resumen

  • 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.