Actualidad

INAMAT2 organiza este seminario que se celebrará el día 15 de febrero a las 10:30 en la sala de conferencias del edificio Jerónimo de Ayanz.

Abstracts:

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.

Thomas Kneib Thomas Kneib is professor for statistics at Georg-August-Universität Göttingen, Germany. His research focuses on semiparametric regression models including distributional regression, random effects models and spatial statistics and statistical learning including regularized maximum likelihood inference, Bayesian inference and functional gradient descent boosting. He is deputy spokesperson of Göttingen's Campus Institute Data Science and deputy spokesperson of the German Consortium for Statistics (Deutsche Arbeitsgemeinschaft Statistik, DAGStat).

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