Linear Estimator

Linear estimators are mathematical tools used to approximate unknown parameters or functions based on observed data, aiming for accuracy and efficiency. Current research emphasizes robust estimation in the presence of noise, outliers, and high dimensionality, exploring techniques like noise-contrastive estimation, regularized least squares, and optimal transport methods within various model architectures (e.g., neural networks, linear models). These advancements are crucial for improving the reliability and performance of machine learning algorithms across diverse applications, including time series analysis, causal inference, and survival analysis, where accurate and robust estimation is paramount.