MIT researchers have developed a method that generates more accurate uncertainty measures for certain types of estimation.

Researchers from Peking University have conducted a comprehensive systematic review on the integration of machine learning into statistical methods for disease risk prediction models, shedding light ...

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 ...

The IMF develops a machine-learning nowcasting framework to estimate quarterly non-oil GDP in GCC countries in real time, ...

Felimban, R. (2025) Financial Prediction Models in Banks: Combining Statistical Approaches and Machine Learning Algorithms.

Machine learning models delivered the strongest performance across nearly all evaluation metrics. CHAID and CART provided the highest and most stable sensitivity, accuracy and discriminatory power, ...

We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

This special report introduces small area estimation as a modern approach for producing reliable, stand-level forest ...

As data privacy collides with AI’s rapid expansion, the Berkeley-trained technologist explains how a new generation of models ...