Statistical modeling continues to deliver distinct value to businesses both independent of, and in concert with, machine learning. “Artificial intelligence” (AI) and “machine learning” are among the ...

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 ...

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 ...

Count data modelling occupies a central role in statistical applications across diverse disciplines including epidemiology, econometrics and engineering. Traditionally, the Poisson distribution has ...

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 ...

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 ...

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 ...

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 ...

A statistical model -- now an easy-to-use software tool -- local police can use to identify a series of related crimes and nab a suspect has been unveiled. Crime linkage is the investigative process ...