Uncertainty quantification of predictive models is crucial in decision-making problems.
Conformal prediction is a general and theoretically sound answer. However, it requires …

Classification and regression trees, as well as their variants, are off-the-shelf methods in
Machine Learning. In this paper, we review recent contributions within the Continuous …

The aim of this paper is to present developments of an advanced geospatial analytics
algorithm that improves the prediction power of a random forest regression model while …

Under the influence of variable sea conditions, a ship will have an oscillating motion
comprising six degrees of freedom, all of which are connected to each other. Among these …

Gaussian process (GP) is a staple in the toolkit of a spatial statistician. Well‐documented
computing roadblocks in the analysis of large geospatial datasets using GPs have now …

Decades of research has debated whether women first need to reach a “critical mass” in the
legislature before they can effectively influence legislative outcomes. This study contributes …

Gridded satellite precipitation datasets are useful in hydrological applications as they cover
large regions with high density. However, they are not accurate in the sense that they do not …

Anthropogenic loading of nitrogen to river systems can pose serious health hazards and
create critical environmental threats. Quantification of the magnitude and impact of …

A major challenge in ecological risk assessment is estimating chemical-induced effects
across taxa without species-specific testing. Where ecotoxicological data may be more …

Latent Gaussian models and boosting are widely used techniques in statistics and machine
learning. Tree-boosting shows excellent prediction accuracy on many data sets, but …