Statistical Analysis and AI Models in the Big Data Era
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 1563
Special Issue Editors
Interests: statistical process monitoring; wavelets analysis; statistical modeling; machine learning applications; forecasting; predictive models
Special Issues, Collections and Topics in MDPI journals
Interests: survival analysis; empirical likelihood method and its application; nonparametric statistics; bioinformatics; ROC curve analysis; Monte Carlo methods; statistical modeling of fuzzy systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the current era of vast amounts of data, statistical analysis has evolved from small-sample inference to addressing high-dimensional, streaming, and diverse data where traditional assumptions often do not apply. Modern AI models, including deep neural networks and large generative systems, act as powerful, non-parametric function approximators. However, their vast capacity brings new statistical issues such as uncertainty quantification, robustness to distributional changes, and interpretability across billions of parameters.
Today’s statistical methods are closely integrated with algorithm development: Bayesian deep learning offers calibrated uncertainty estimates through variational inference and Monte Carlo dropout with post-specific experimental settings to recalibrate the outputs; conformal prediction ensures finite-sample coverage without relying on distributional assumptions; and robust statistics defend against adversarial or corrupted data. Simultaneously, causal inference and counterfactual reasoning are being adapted for large observational datasets, supporting policy decisions in medicine, finance, and climate science.
In essence, the integration of rigorous statistical theory and scalable AI architectures is propelling a transition from basic prediction to principled, reliable decision-making under uncertainty in the Big Data era.
We eagerly anticipate contributions that advance our understanding in these critical domains.
Dr. Achraf Cohen
Prof. Dr. Yichuan Zhao
Guest Editors
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Keywords
- uncertainty quantification
- robustness
- machine learning
- deep learning
- advanced statistics
- AI
- big data
- conformal prediction
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