Conveners
Young statisticians Invited Session
- Fabio Centofanti (KU Leuven)
This work presents a novel active learning market framework that enhances both forecasting accuracy and parameter estimation quality in data-constrained environments—typical in many industrial and business applications. Traditional models often rely on full data access, but our approach prioritizes selective acquisition of high-value data, striking a balance between statistical efficiency and...
Healthcare fraud is a significant issue that leads to substantial financial losses and compromises the quality of patient care. Traditional fraud detection methods often rely on rule-based systems and manual audits, which are inefficient and lack scalability. Machine learning methods have begun to be incorporated in the fraud detection systems of insurance companies; however these methods...
Modern industrial systems generate real-time multichannel profile data for process monitoring and fault diagnosis. While most methods focus on detecting process mean shifts, identifying changes in the covariance structure is equally important, as process behavior often depends on interdependence among multiple variables. However, monitoring covariance in multichannel profiles is exacerbated by...