Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity

ArXi:2605.27526v1 Announce Type: cross We develop semiparametrically efficient inference for kernel measures of noise heterogeneity in additive noise models. In many applications, the regression function is estimated using flexible machine learning methods. Downstream procedures based on the resulting residuals can then inherit first-stage bias: regression error may induce spurious dependence between covariates and residuals, invalidating the assumptions needed for standard analysis.