Nadja Klein

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Humboldt-Universität zu Berlin

Bayesian Conditional Transformation Models

Recent developments in statistical regression methodology establish flex...
12/20/2020 ∙ by Manuel Carlan, et al. ∙ 0 ∙ share

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Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices

Recurrent neural networks (RNNs) with rich feature vectors of past value...
10/05/2020 ∙ by Nadja Klein, et al. ∙ 0 ∙ share

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A Unifying Network Architecture for Semi-Structured Deep Distributional Learning

We propose a unifying network architecture for deep distributional learn...
02/13/2020 ∙ by David Rügamer, et al. ∙ 22 ∙ share

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Assessment and adjustment of approximate inference algorithms using the law of total variance

A common method for assessing validity of Bayesian sampling or approxima...
11/20/2019 ∙ by Xuejun Yu, et al. ∙ 0 ∙ share

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

Over the last decades, the challenges in applied regression and in predi...
09/25/2019 ∙ by Nikolaus Umlauf, et al. ∙ 0 ∙ share

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In Search of Lost Edges: A Case Study on Reconstructing Financial Networks

To capture the systemic complexity of international financial systems, n...
09/03/2019 ∙ by Michael Lebacher, et al. ∙ 0 ∙ share

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Marginally-calibrated deep distributional regression

Deep neural network (DNN) regression models are widely used in applicati...
08/26/2019 ∙ by Nadja Klein, et al. ∙ 21 ∙ share

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Bayesian Variable Selection for Non-Gaussian Responses: A Marginally Calibrated Copula Approach

We propose a new highly flexible and tractable Bayesian approach to unde...
07/10/2019 ∙ by Nadja Klein, et al. ∙ 0 ∙ share

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Bayesian Inference for Regression Copulas

We propose a new semi-parametric distributional regression smoother for ...
07/10/2019 ∙ by Michael Stanley Smith, et al. ∙ 0 ∙ share

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Multivariate Conditional Transformation Models

Regression models describing the joint distribution of multivariate resp...
06/07/2019 ∙ by Nadja Klein, et al. ∙ 0 ∙ share

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Bayesian Effect Selection in Structured Additive Distributional Regression Models

We propose a novel spike and slab prior specification with scaled beta p...
02/27/2019 ∙ by Nadja Klein, et al. ∙ 0 ∙ share

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More green space is related to less antidepressant prescription rates in the Netherlands: A Bayesian geoadditive quantile regression approach

Exposure to green space seems to be beneficial for self-reported mental ...
05/18/2018 ∙ by Marco Helbich, et al. ∙ 0 ∙ share

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Implicit Copulas from Bayesian Regularized Regression Smoothers

We show how to extract the implicit copula of a response vector from a B...
04/27/2018 ∙ by Nadja Klein, et al. ∙ 0 ∙ share

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Boosting Joint Models for Longitudinal and Time-to-Event Data

Joint Models for longitudinal and time-to-event data have gained a lot o...
09/09/2016 ∙ by Elisabeth Waldmann, et al. ∙ 0 ∙ share

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Nadja Klein

Featured Co-authors

Minh-Ngoc Tran

Göran Kauermann

David J. Nott

Thomas Kneib

Andreas Mayr

David Rügamer

Torsten Hothorn

Achim Zeileis

Michael Stanley Smith

Matthias Schmid

Michael Lebacher

Bayesian Conditional Transformation Models

Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices

A Unifying Network Architecture for Semi-Structured Deep Distributional Learning

Assessment and adjustment of approximate inference algorithms using the law of total variance

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

In Search of Lost Edges: A Case Study on Reconstructing Financial Networks

Marginally-calibrated deep distributional regression

Bayesian Variable Selection for Non-Gaussian Responses: A Marginally Calibrated Copula Approach

Bayesian Inference for Regression Copulas

Multivariate Conditional Transformation Models

Bayesian Effect Selection in Structured Additive Distributional Regression Models

More green space is related to less antidepressant prescription rates in the Netherlands: A Bayesian geoadditive quantile regression approach

Implicit Copulas from Bayesian Regularized Regression Smoothers

Boosting Joint Models for Longitudinal and Time-to-Event Data