# Multivariate conditional transformation models

@article{Klein2019MultivariateCT, title={Multivariate conditional transformation models}, author={N. Klein and T. Hothorn and Luisa Barbanti and T. Kneib}, journal={arXiv: Methodology}, year={2019} }

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 dependency 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… Expand

#### 6 Citations

Marginally Interpretable Linear Transformation Models for Clustered Observations

- Mathematics
- 2019

Clustered observations are ubiquitous in controlled and observational studies and arise naturally in multicenter trials or longitudinal surveys. I present two novel models for the analysis of… Expand

A nested copula duration model for competing risks with multiple spells

- Computer Science, Mathematics
- Comput. Stat. Data Anal.
- 2020

It is shown that the dependenceructure between spells is identifiable and can be estimated, in contrast to the dependence structure between competing risks, and the model is not identifiable. Expand

Deep transformation models: Tackling complex regression problems with neural network based transformation models

- Computer Science, Mathematics
- 2020 25th International Conference on Pattern Recognition (ICPR)
- 2021

A deep transformation model for probabilistic regression that estimates the whole conditional probability distribution, which is the most thorough way to capture uncertainty about the outcome. Expand

XGBoostLSS -- An extension of XGBoost to probabilistic forecasting

- Mathematics, Computer Science
- 2019

A new framework of XGBoost is proposed that predicts the entire conditional distribution of a univariate response variable and models all moments of a parametric distribution instead of the conditional mean only. Expand

XGBoostLSS - An extension of XGBoost to probabilistic forecasting

- Mathematics, Computer Science
- ArXiv
- 2019

A new framework of XGBoost is proposed that predicts the entire conditional distribution of a univariate response variable and models all moments of a parametric distribution instead of the conditional mean only. Expand

Deep Conditional Transformation Models

- Computer Science, Mathematics
- ECML/PKDD
- 2021

The class of deep conditional transformation models are introduced which unify existing approaches and allow to learn both interpretable (non-)linear model terms and more complex predictors in one holistic neural network. Expand

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