17 citations · 19 across the 7 of their papers we have counts for
7 papers
To select or not to select: predictively consistent priors instead of model selection
Anna Elisabeth Riha, Leevi Lindgren, David Kohns +2
Bayesian modelling workflows often consider multiple candidate models of varying complexity. Model selection is commonly used to navigate potential trade-offs between model complex…
R2 priors for Grouped Variance Decomposition in High-dimensional Regression
Javier Enrique Aguilar, David Kohns, Aki Vehtari +1
We introduce the Group-R2 decomposition prior, a hierarchical shrinkage prior that extends R2-based priors to structured regression settings with known groups of predictors. By dec…
Joint Quantile Shrinkage: A State-Space Approach toward Non-Crossing Bayesian Quantile Models
David Kohns, Tibor Szendrei
Crossing of fitted conditional quantiles is a prevalent problem for quantile regression models. We propose a new Bayesian modelling framework that penalises multiple quantile regre…
The ARR2 prior: flexible predictive prior definition for Bayesian auto-regressions
David Kohns, Noa Kallioinen, Yann McLatchie +1
We present the ARR2 prior, a joint prior over the auto-regressive components in Bayesian time-series models and their induced . Compared to other priors designed for times-ser…
Decoupling Shrinkage and Selection for the Bayesian Quantile Regression
David Kohns, Tibor Szendrei
This paper extends the idea of decoupling shrinkage and sparsity for continuous priors to Bayesian Quantile Regression (BQR). The procedure follows two steps: In the first step, we…
Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model
David Kohns, Arnab Bhattacharjee
This paper investigates the benefits of internet search data in the form of Google Trends for nowcasting real U.S. GDP growth in real time through the lens of mixed frequency Bayes…
Horseshoe Prior Bayesian Quantile Regression
David Kohns, Tibor Szendrei
This paper extends the horseshoe prior of Carvalho et al. (2010) to Bayesian quantile regression (HS-BQR) and provides a fast sampling algorithm for computation in high dimensions.…