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 ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine ...

The aim of this paper is to present some of the stylized features of financial data which have received a lot of attention both from practitioners and those with more theoretical backgrounds. Some of ...

Bracket Breakers is written by Peter Keating and Jordan Brenner. This series identified the major upsets in each region, using their Slingshot model, which was developed alongside the Furman ...

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 ...

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 ...

A range of genetic factors can influence the onset of diseases like high blood pressure, heart disease, and type 2 diabetes, according to scientists. If we were to know how the DNA influences the risk ...

A new study published in the journal Science of The Total Environment has significant bearing on the hackneyed joke about chickens and their numerous reasons for crossing roads. In Florida, there's a ...

A new study introduces a global probabilistic forecasting model that predicts when and where ionospheric disturbances—measured by the Rate of total electron content (TEC) Index (ROTI)—are likely to ...