Bayesian inference is a powerful tool for combining information in complex settings, a task of
increasing importance in modern applications. However, Bayesian inference with a flawed …

Time-varying parameter (TVP) models have the potential to be over-parameterized,
particularly when the number of variables in the model is large. Global-local priors are …

Nonparametric regression models have recently surged in their power and popularity,
accompanying the trend of increasing dataset size and complexity. While these models have …

Social and environmental stressors are crucial factors in child development. However, there
exists a multitude of measurable social and environmental factors—the effects of which may …

Prediction is critical for decision-making under uncertainty and lends validity to statistical
inference. With targeted prediction, the goal is to optimize predictions for specific decision …

This article introduces Bayesian inference procedures for tangency portfolios, with a primary
focus on deriving a new conjugate prior for portfolio weights. This approach not only enables …

Subset selection is a valuable tool for interpretable learning, scientific discovery, and data
compression. However, classical subset selection is often avoided due to selection …

We propose a new Bayesian model for flexible nonlinear regression and classification using
tree ensembles. The model is based on the RuleFit approach in Friedman and Popescu …

Bayesian adaptive and interpretable functional regression for exposure profiles Page 1 The
Annals of Applied Statistics 2024, Vol. 18, No. 1, 642–663 https://doi.org/10.1214/23-AOAS1805 …

Conjugate priors allow for fast inference in large dimensional vector autoregressive (VAR)
models. But at the same time, they introduce the restriction that each equation features the …