Robust Regression

Robust regression aims to estimate relationships between variables while mitigating the influence of outliers or noisy data points, a crucial task in many real-world applications where data quality is imperfect. Current research emphasizes developing algorithms that are both computationally efficient and statistically robust, focusing on techniques like iterative gradient descent, M-estimators (e.g., using Huber loss), and distributionally robust optimization within various model frameworks including linear regression, generalized linear models, and even deep neural networks. These advancements improve the reliability and accuracy of statistical inferences and predictive models in diverse fields, ranging from causal inference in time series analysis to robust machine learning in high-dimensional settings.