Residual Estimation

Residual estimation focuses on analyzing the difference between predicted and actual values (residuals) to improve model accuracy, identify anomalies, or gain insights into underlying data structures. Current research explores diverse applications, from accelerating video processing using recursive networks and multi-scale residual modules to detecting price anomalies in real estate markets via novel scoring metrics and robust penalized regression methods for high-dimensional data. These advancements enhance model robustness, improve prediction accuracy, and provide valuable tools for various fields, including computer vision, data analysis, and economic modeling.