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Research Frontiers in Robust Statistics and Outlier Detection

Development of statistical procedures resistant to deviations from model assumptions and contaminated data.

Adaptive Contamination Models in High-Dimensional Spaces
Breakdown Points and Structural Phase Transitions
Implicit Bias in Outlier Rejection Algorithms
Robustness Under Simultaneous Location and Scale Corruption
Outlier Detection in Functional and Infinite-Dimensional Data
Robust Causal Inference Under Hidden Contamination
Leverage Points and Influence in Network-Dependent Observations
Computational Hardness of Optimal Robust Estimation
Outlier-Aware Uncertainty Quantification in Machine Learning
Minimax Optimality Across Contamination Regimes

All Statistics PhD categories