Humboldt-Universität zu Berlin | School of Business and Economics | Emmy Noether Research Group | News News Prof. Dr. Nadja Klein was accepted for the Leadership Programme for Female Professors 2022 Prof. Dr. Nadja Klein becomes liaison professor of the German Academic Scholarship Foundation Prof. Dr. Nadja Klein becomes member of the International Biometric Society (IBS) Nadja Klein becomes speaker of the Research Group "Artificial Intelligence" (Junge Akademie) Paper "Correcting for Sample Selection Bias in Bayesian Distributional Regression Models Computational Statistics and Data Analysis" by Nadja Klein, Paul Wiemann, and Thomas Kneib accepted for publication New working paper "Marginally calibrated response distributions for end-to-end learning in autonomous driving" by Clara Hoffmann and Nadja Klein New paper "Statistical model building: Background “knowledge” based on inappropriate preselection causes misspecification" by Nadja Klein et al. published. New team member Silke Kaiser since 1st of October Invited Session proposal "Recent Advances in Variational Inference Methods" will be part of the upcoming 16th World Meeting of the International Society for Bayesian Analysis Paper "Deselection of Base-Learners for Statistical Boosting - with an Application to Distributional Regression" by Nadja Klein et al. accepted for publication New working paper "Posterior Concentration Rates for Bayesian O'Sullivan Penalized Splines" by Paul Bach and Nadja Klein Prof. Dr. Nadja Klein becomes member of the International Society of Bayesian Analysis (ISBA) New working paper "A multivariate Gaussian random field prior against spatial confounding" New working paper "Bayesian Effect Selection for Additive Quantile Regression with an Analysis to Air Pollution Thresholds" New working paper "Variational Inference and Sparsity in High-Dimensional Deep Gaussian Mixture Models" New working paper "Bivariate Analysis of Birth Weight and Gestational Age Depending on Environmental Exposures: Bayesian Distributional Regression with Copulas" Paper "bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)" accepted for publication New working paper "Semi-Structured Deep Distributional Regression: Combining Structured Additive Models and Deep Learning" New working paper "deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression" Prof. Dr. Nadja Klein becomes member of editorial advisory board of Journal Dependence Modeling 1 2 3 Next 20 items
Paper "Correcting for Sample Selection Bias in Bayesian Distributional Regression Models Computational Statistics and Data Analysis" by Nadja Klein, Paul Wiemann, and Thomas Kneib accepted for publication
New working paper "Marginally calibrated response distributions for end-to-end learning in autonomous driving" by Clara Hoffmann and Nadja Klein
New paper "Statistical model building: Background “knowledge” based on inappropriate preselection causes misspecification" by Nadja Klein et al. published.
Invited Session proposal "Recent Advances in Variational Inference Methods" will be part of the upcoming 16th World Meeting of the International Society for Bayesian Analysis
Paper "Deselection of Base-Learners for Statistical Boosting - with an Application to Distributional Regression" by Nadja Klein et al. accepted for publication
New working paper "Posterior Concentration Rates for Bayesian O'Sullivan Penalized Splines" by Paul Bach and Nadja Klein
New working paper "Bayesian Effect Selection for Additive Quantile Regression with an Analysis to Air Pollution Thresholds"
New working paper "Variational Inference and Sparsity in High-Dimensional Deep Gaussian Mixture Models"
New working paper "Bivariate Analysis of Birth Weight and Gestational Age Depending on Environmental Exposures: Bayesian Distributional Regression with Copulas"
Paper "bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)" accepted for publication
New working paper "Semi-Structured Deep Distributional Regression: Combining Structured Additive Models and Deep Learning"
New working paper "deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression"
