Nadja Klein

Technische Universität Dortmund

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Articles (21)

Collaborators (10)

Umlauf Nikolaus

Assoc. Prof.

University of Innsbruck

David Rügamer

Associate Professor

Andreas Mayr

University of Bonn

Tim Friede

Universitätsmedizin Göttingen

Michael Kammer

Medical University of Vienna

Philipp Doebler

Technische Universität Dortmund

Leonie Weinhold

University Hospital Bonn

Georg Heinze

David Nott

National University of Singapore (NUS)

Michael Smith

Professor of Management (Econometrics)

University of Melbourne

Nadja Klein

Research Interests

Explore related searches

Contact this professor

Articles (21)

Collaborators (10)

Umlauf Nikolaus

David Rügamer

Andreas Mayr

Tim Friede

Michael Kammer

Philipp Doebler

Leonie Weinhold

Georg Heinze

David Nott

Michael Smith

How do I reach out?

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Nadja Klein

Research Interests

Explore related searches

Contact this professor

Articles (21)

Bayesian Regularized Regression Copula Processes for Multivariate Responses

Dynamics of Insight, Emotion Regulation, and Emotional Clarity in Obsessive–Compulsive Disorder

Enhanced Variable Selection for Boosting Sparser and Less Complex Models in Distributional Copula Regression

Boosting Causal Additive Models

Building bridges between Bayesian Statistics and Deep Learning

Boosting distributional copula regression for bivariate binary, discrete and mixed responses

Modeling the ratio of correlated biomarkers using copula regression

Scalable Estimation for Structured Additive Distributional Regression

Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection

Density regression via Dirichlet process mixtures of normal structured additive regression models

Accounting for time dependency in meta‐analyses of concordance probability estimates

Boosting multivariate structured additive distributional regression models

Learning Causal Graphs in Manufacturing Domains Using Structural Equation Models

Boosting distributional copula regression

Bayesian Conditional Transformation Models

Semi-Structured Distributional Regression

Using Background Knowledge from Preceding Studies for Building a Random Forest Prediction Model: A Plasmode Simulation Study

Variational inference and sparsity in high-dimensional deep Gaussian mixture models

bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)

Deselection of base-learners for statistical boosting—with an application to distributional regression

Correcting for sample selection bias in Bayesian distributional regression models

Collaborators (10)

Umlauf Nikolaus

David Rügamer

Andreas Mayr

Tim Friede

Michael Kammer

Philipp Doebler

Leonie Weinhold

Georg Heinze

David Nott

Michael Smith

How do I reach out?

Meet Kite AI