A big part of his work is figuring out when a statistical method is truly the best choice. Some problems have straightforward ...

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 ...

A deep learning framework combines convolutional and bidirectional recurrent networks to improve protein function prediction from genomic ...

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 ...

Livestock agriculture (e.g., dairy, beef, pork, poultry) in the United States is tending rapidly toward operations where a large number of animals are concentrated in a relatively small area. The ...

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 ...

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 ...

Statistical models predict stock trends using historical data and mathematical equations. Common statistical models include regression, time series, and risk assessment tools. Effective use depends on ...

A model is proposed for the study of plant breeding where the self-fertilization rate is of importance. The model can be used to provide convenient maximum likelihood estimation of the ...

A study has validated a new statistical model that uses multiple standardized test scores over time to predict future academic performance. The dynamic measurement model accurately predicted academic ...