138 citations · 302 across the 32 of their papers we have counts for
8 papers · 1 filter
Assessment and adjustment of approximate inference algorithms using the law of total variance
Xuejun Yu, David J. Nott, Minh-Ngoc Tran +1
A common method for assessing validity of Bayesian sampling or approximate inference methods makes use of simulated data replicates for parameters drawn from the prior. Under conti…
bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)
Nikolaus Umlauf, Nadja Klein, Thorsten Simon +1
Over the last decades, the challenges in applied regression and in predictive modeling have been changing considerably: (1) More flexible model specifications are needed as big(ger…
In Search of Lost Edges: A Case Study on Reconstructing Financial Networks
Michael Lebacher, Samantha Cook, Nadja Klein +1
To capture the systemic complexity of international financial systems, network data is an important prerequisite. However, dyadic data is often not available, raising the need for…
Marginally-calibrated deep distributional regression
Nadja Klein, David J. Nott, Michael Stanley Smith
Deep neural network (DNN) regression models are widely used in applications requiring state-of-the-art predictive accuracy. However, until recently there has been little work on ac…
Bayesian Variable Selection for Non-Gaussian Responses: A Marginally Calibrated Copula Approach
Nadja Klein, Michael Stanley Smith
We propose a new highly flexible and tractable Bayesian approach to undertake variable selection in non-Gaussian regression models. It uses a copula decomposition for the joint dis…
Bayesian Inference for Regression Copulas
Michael Stanley Smith, Nadja Klein
We propose a new semi-parametric distributional regression smoother that is based on a copula decomposition of the joint distribution of the vector of response values. The copula i…
Multivariate Conditional Transformation Models
Nadja Klein, Torsten Hothorn, Luisa Barbanti +1
Regression models describing the joint distribution of multivariate response variables conditional on covariate information have become an important aspect of contemporary regressi…
Bayesian Effect Selection in Structured Additive Distributional Regression Models
Nadja Klein, Manuel Carlan, Thomas Kneib +2
We propose a novel spike and slab prior specification with scaled beta prime marginals for the importance parameters of regression coefficients to allow for general effect selectio…