Specifying, assessing, and selecting among candidate statistical models is fundamental to
ecological research. Commonly used approaches to model selection are based on …

Variable selection methods have been extensively developed for and applied to cancer
genomics data to identify important omics features associated with complex disease traits …

Adopting innovative technologies like machine learning is crucial for achieving our
sustainability goals. It has great potential for improving waste management and energy …

Agricultural greenhouses must accurately predict environmental factors to ensure optimal
crop growth and energy management efficiency. However, the existing predictors have …

Statistical methods are based on model assumptions, and it is statistical folklore that a
method's model assumptions should be checked before applying it. This can be formally …

Managing pandemics is an enduring societal problem because major health emergencies
have historically led to substantial changes and developments. While extant research has …

The use of closed growth environments, such as greenhouses, plant factories, and vertical
farms, represents a sustainable alternative for fresh food production. Closed plant …

Background: Previous studies have identified the consumption of country foods
(hunted/harvested foods from the land) as the primary exposure source of perfluoroalkyl …

We investigate a class of methods for selective inference that condition on a selection event.
Such methods follow a two-stage process. First, a data-driven collection of hypotheses is …

We investigate a class of methods for selective inference that condition on a selection event.
Such methods operate in a two-stage process. First, a (sub) collection of hypotheses is …