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stat.ME2026

Copula-based models for spatially dependent cylindrical data

Francesca Labanca, Anna Gottard, Nadja Klein

Cylindrical data frequently arise across various scientific disciplines, including meteorology (e.g., wind direction and speed), oceanography (e.g., marine current direction and sp…

stat.ME2026

Bayesian Additive Regression Tree Copula Processes for Scalable Distributional Prediction

Jan Martin Wenkel, Michael Stanley Smith, Nadja Klein

We show how to construct the implied copula process of response values from a Bayesian additive regression tree (BART) model with prior on the leaf node variances. This copula proc…

stat.ME2024

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

María Xosé Rodríguez-Álvarez, Vanda Inácio, Nadja Klein

Within Bayesian nonparametrics, dependent Dirichlet process mixture models provide a highly flexible approach for conducting inference about the conditional density function. Howev…

stat.ME2024

Bayesian Effect Selection in Additive Models with an Application to Time-to-Event Data

Paul Bach, Nadja Klein

Accurately selecting and estimating smooth functional effects in additive models with potentially many functions is a challenging task. We introduce a novel Demmler-Reinsch basis e…

stat.ME2023

Scalable Estimation for Structured Additive Distributional Regression Through Variational Inference

Jana Kleinemeier, Nadja Klein

Structured additive distributional regression models offer a versatile framework for estimating complete conditional distributions by relating all parameters of a parametric distri…