High Dimensional Robust

High-dimensional robust statistics focuses on developing methods to accurately estimate parameters from data sets where the number of variables far exceeds the number of observations and data may be contaminated by outliers or heavy-tailed noise. Current research emphasizes robust regression techniques, often employing M-estimators (like Huber loss) or proximal stochastic gradient descent, and explores efficient streaming algorithms to handle massive datasets. These advancements are crucial for improving the reliability and scalability of statistical analyses in various fields, including machine learning and data science, where high-dimensional, noisy data are common.