Local Regression

Local regression methods aim to estimate a function's value at a specific point by weighting nearby data points more heavily, offering flexibility in modeling complex relationships compared to global methods. Current research emphasizes improving local adaptivity, particularly through advancements in algorithms like trend filtering and prediction-powered inference, and exploring efficient model architectures for high-dimensional data and complex scenarios such as network-based regressions. These improvements enhance the accuracy and interpretability of local regression models, impacting diverse fields from anomaly detection in industrial settings to time series forecasting in telecommunications and financial modeling.