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07/20/2023
Distributional Regression for Data Analysis
Flexible modeling of how an entire distribution changes with covariates ...
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06/05/2023
Truly Multivariate Structured Additive Distributional Regression
Generalized additive models for location, scale and shape (GAMLSS) are a...
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05/19/2023
The Deep Promotion Time Cure Model
We propose a novel method for predicting time-to-event in the presence o...
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05/11/2023
Dropout Regularization in Extended Generalized Linear Models based on Double Exponential Families
Even though dropout is a popular regularization technique, its theoretic...
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01/13/2023
Scalable Estimation for Structured Additive Distributional Regression
Recently, fitting probabilistic models have gained importance in many ar...
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12/03/2022
Accounting for Time Dependency in Meta-Analyses of Concordance Probability Estimates
Recent years have seen the development of many novel scoring tools for d...
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11/29/2022
Anisotropic multidimensional smoothing using Bayesian tensor product P-splines
We introduce a highly efficient fully Bayesian approach for anisotropic ...
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11/01/2022
Informed Priors for Knowledge Integration in Trajectory Prediction
Informed machine learning methods allow the integration of prior knowled...
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10/19/2022
Distributional Adaptive Soft Regression Trees
Random forests are an ensemble method relevant for many problems, such a...
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07/18/2022
Boosting Multivariate Structured Additive Distributional Regression Models
We develop a model-based boosting approach for multivariate distribution...
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04/29/2022
Incorporating Actor Heterogeneity into Large Network Models through Variational Approximations
The analysis of network data has gained considerable interest in the rec...
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02/25/2022
Boosting Distributional Copula Regression
Capturing complex dependence structures between outcome variables (e.g.,...
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02/03/2022
Deselection of Base-Learners for Statistical Boosting – with an Application to Distributional Regression
We present a new procedure for enhanced variable selection for component...
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10/03/2021
Marginally calibrated response distributions for end-to-end learning in autonomous driving
End-to-end learners for autonomous driving are deep neural networks that...
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09/09/2021
Posterior Concentration Rates for Bayesian O'Sullivan Penalized Splines
The O'Sullivan penalized splines approach is a popular frequentist appro...
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06/07/2021
A multivariate Gaussian random field prior against spatial confounding
Spatial models are used in a variety research areas, such as environment...
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05/23/2021
Bayesian Effect Selection for Additive Quantile Regression with an Analysis to Air Pollution Thresholds
Statistical techniques used in air pollution modelling usually lack the ...
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05/19/2021
Flexible Specification Testing in Semi-Parametric Quantile Regression Models
We propose several novel consistent specification tests for quantile reg...
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05/04/2021
Variational Inference and Sparsity in High-Dimensional Deep Gaussian Mixture Models
Gaussian mixture models are a popular tool for model-based clustering, a...
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04/29/2021
Bivariate Analysis of Birth Weight and Gestational Age Depending on Environmental Exposures: Bayesian Distributional Regression with Copulas
In this article, we analyze perinatal data with birth weight (BW) as pri...
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04/06/2021
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
This paper describes the implementation of semi-structured deep distribu...
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12/20/2020
Bayesian Conditional Transformation Models
Recent developments in statistical regression methodology establish flex...
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10/05/2020
Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices
Recurrent neural networks (RNNs) with rich feature vectors of past value...
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02/13/2020
A Unifying Network Architecture for Semi-Structured Deep Distributional Learning
We propose a unifying network architecture for deep distributional learn...
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11/20/2019
Assessment and adjustment of approximate inference algorithms using the law of total variance
A common method for assessing validity of Bayesian sampling or approxima...
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09/25/2019
bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)
Over the last decades, the challenges in applied regression and in predi...
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09/03/2019
In Search of Lost Edges: A Case Study on Reconstructing Financial Networks
To capture the systemic complexity of international financial systems, n...
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08/26/2019
Marginally-calibrated deep distributional regression
Deep neural network (DNN) regression models are widely used in applicati...
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07/10/2019
Bayesian Variable Selection for Non-Gaussian Responses: A Marginally Calibrated Copula Approach
We propose a new highly flexible and tractable Bayesian approach to unde...
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07/10/2019
Bayesian Inference for Regression Copulas
We propose a new semi-parametric distributional regression smoother for ...
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06/07/2019
Multivariate Conditional Transformation Models
Regression models describing the joint distribution of multivariate resp...
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02/27/2019
Bayesian Effect Selection in Structured Additive Distributional Regression Models
We propose a novel spike and slab prior specification with scaled beta p...
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05/18/2018
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 ...
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04/27/2018
Implicit Copulas from Bayesian Regularized Regression Smoothers
We show how to extract the implicit copula of a response vector from a B...
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09/09/2016