7 papers · 1 filter
Boosting Distributional Copula Regression for Bivariate Right-Censored Time-to-Event Data
Guillermo Briseno-Sanchez, Nadja Klein, Andreas Groll +1
We propose a highly flexible distributional copula regression model for bivariate time-to-event data in the presence of right-censoring. The joint survival function of the response…
Enhanced variable selection for boosting sparser and less complex models in distributional copula regression
Annika Strömer, Nadja Klein, Christian Staerk +3
Structured additive distributional copula regression allows to model the joint distribution of multivariate outcomes by relating all distribution parameters to covariates. Estimati…
Boosting Distributional Copula Regression for Bivariate Binary, Discrete and Mixed Responses
Guillermo Briseño Sanchez, Nadja Klein, Hannah Klinkhammer +1
Motivated by challenges in the analysis of biomedical data and observational studies, we develop statistical boosting for the general class of bivariate distributional copula regre…
Regression Copulas for Multivariate Responses
Nadja Klein, Michael Stanley Smith, David Nott +1
We propose a novel distributional regression model for a multivariate response vector based on a copula process over the covariate space. It uses the implicit copula of a Gaussian…
Bayesian Effect Selection in Additive Models with an Application to Time-to-Event Data
Paul Bach, Nadja Klein
Accurately selecting and estimating smooth functional effects in additive models with potentially many functions is a challenging task. We introduce a novel Demmler-Reinsch basis e…
Scalable Estimation for Structured Additive Distributional Regression Through Variational Inference
Jana Kleinemeier, Nadja Klein
Structured additive distributional regression models offer a versatile framework for estimating complete conditional distributions by relating all parameters of a parametric distri…