Minimax Additive Regression under Unknown Dependent Designs
The paper establishes optimal rates for predicting additive models when both the data distribution and the regression function are unknown and dependent. It uses thresholded least-squares estimators based on a distribution-adapted Riesz basis to recover additive components at minimax optimal rates, depending on whether marginal densities are known or unknown.