Truncated M Estimator

Truncated M-estimators are robust statistical methods designed to mitigate the influence of outliers and noise in data, primarily aiming for more reliable parameter estimation in various models. Current research focuses on extending their application to challenging scenarios like high-dimensional data, noisy labels, and distributed or federated learning settings, often employing algorithms such as convex programming, Frank-Wolfe methods, and adaptive learning techniques. These advancements enhance the robustness and efficiency of statistical inference across diverse fields, including machine learning, econometrics, and signal processing, leading to improved model accuracy and reliability in the presence of data imperfections.