Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...

Bayesian quantile regression and statistical modelling represent a growing paradigm in contemporary data analysis, extending conventional regression by estimating various conditional quantiles rather ...

In this module, we will introduce the basic conceptual framework for statistical modeling in general, and linear statistical models in particular. In this module, we will learn how to fit linear ...

Researchers have created a statistical method that may allow public health and infectious disease forecasters to better predict disease reemergence, especially for preventable childhood infections ...

Researchers have developed a new statistical model that predicts which cities are more likely to become infectious disease hotspots, based both on interconnectivity between cities and the idea that ...

This is a preview. Log in through your library . Abstract In this paper, we extend and analyze a Bayesian hierarchical spatiotemporal model for physical systems. A novelty is to model the discrepancy ...

Mark Holmes of UNC-Chapel Hill's Gillings School of Global Public Health discusses a new statistical model that gives a more accurate picture of when the coronavirus outbreak could peak in North ...

In a recent article published in the eLife Journal, researchers launched a possum excreta surveillance program across 350 km 2 in the Mornington Peninsula near South Melbourne, Australia. The study ...

Nutrient loading has been linked to many issues including eutrophication, harmful algal blooms, and decreases in aquatic species diversity. In order to develop mitigation strategies to control ...