Variance Estimation

Variance estimation focuses on accurately quantifying the uncertainty associated with statistical estimates, crucial for reliable inference and decision-making across diverse fields. Current research emphasizes developing robust and efficient variance estimators for various settings, including machine learning models (e.g., neural networks, random forests, k-nearest neighbors), reinforcement learning algorithms, and complex data structures like graphs and longitudinal data. This work addresses challenges like bias in existing methods, particularly in high-dimensional or adaptively sampled data, and aims to improve the accuracy and efficiency of uncertainty quantification. Improved variance estimation enhances the reliability of scientific findings and enables more informed decisions in applications ranging from medical diagnostics to financial modeling.