Multivariate Regression

Multivariate regression aims to model the relationship between multiple predictor variables and multiple response variables, often seeking to improve prediction accuracy and understand complex interactions. Current research emphasizes developing robust and efficient algorithms, including those based on neural networks, kernel methods (like Kernel Partial Least Squares), and enhanced linear models incorporating regularization techniques to handle high-dimensionality and noise. These advancements are impacting diverse fields, from environmental risk assessment and material science (through improved uncertainty quantification) to agricultural forecasting and financial modeling, by enabling more accurate predictions and insightful analyses of complex datasets.