Robust Functional Regression via FPCA, Copula-Scale Design, and Multivariate Bernstein Smoothing

Abstract

We introduce a robust nonparametric regression framework for functional covariates that combines functional principal component analysis (FPCA), marginal copula-scale normalization, bounded-score M-estimation, and multivariate Bernstein smoothing. The proposed procedure reduces the infinite-dimensional functional predictor to a low-dimensional score representation, transforms the retained scores onto the compact unit cube, and estimates a conditional M-functional through a smoothly aggregated system of local estimating equations. This construction is designed to accommodate nonlinear regression structure, heavy-tailed score distributions, and response contamination while limiting the influence of extreme observations. Under suitable regularity and undersmoothing conditions, we establish pointwise and uniform consistency, derive explicit convergence rates, and prove asymptotic normality. The limiting variance contains an explicit Bernstein concentration factor that plays a role analogous to the integrated squared kernel in classical nonparametric regression. The analysis also clarifies the interaction among the projection dimension, the Bernstein resolution, the empirical copula transformation, and the effective local sample size. The finite-sample performance of the method is examined through simulations involving heavy-tailed functional scores, Student-t errors, nonlinear regression effects, and increasing response contamination. The proposed estimator exhibits strong overall predictive performance and good robustness, with particularly favorable behavior under absolute-error criteria.