Distributional structured additive regression provides a flexible framework for modeling each parameter of a potentially complex response distribution in dependence of covariates. Structured additive predictors allow for an additive decomposition of covariate effects with nonlinear effects and...

This paper develops a sampling algorithm for bandwidth estimation in a nonparametric regression model with continuous and discrete regressors under an unknown error density. The error density is approximated by the kernel density estimator of the unobserved errors, while the regression function...

We propose a shrinkage and selection methodology specifically designed for network inference using high dimensional data through a regularised linear regression model with Spike-and-Slab prior on the parameters. The approach extends the case where the error terms are heteroscedastic, by adding...

Probability forecasts of binary events are often gathered from multiple models and averaged to provide inputs regarding uncertainty in important decision-making problems. Averages of well calibrated probabilities are underconfident, and methods have been proposed to make them more extreme. To...

We adopt the Jackknife Model Averaging (JMA) technique to conduct a meta-regression analysis of 925 renminbi (RMB) misalignment estimates generated by 69 studies. The JMA method accounts for model selection and sampling uncertainties, and allows for non-nested model specifications and...