Asymptotic Inference

Asymptotic inference focuses on developing methods for drawing reliable conclusions from data as the sample size grows infinitely large, providing theoretical guarantees for statistical procedures. Current research emphasizes improving the accuracy and efficiency of these methods, particularly for complex models like those involving high-dimensional data, switching systems, and non-Euclidean spaces, often employing techniques such as Bayesian coresets, adaptive linear estimating equations, and Karcher means. These advancements are crucial for ensuring the validity and reliability of statistical analyses across diverse scientific fields and practical applications, ranging from machine learning and control systems to causal inference and fairness analysis.