Introduces a Hilbert-valued one-step estimator for the kernel covariance operator between covariates and residuals that enables semiparametrically efficient inference on noise heterogeneity and residual independence in additive noise models.
Roman Pogodin, Antonin Schrab, Yazhe Li, Danica J Sutherland, and Arthur Gretton
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Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity
Introduces a Hilbert-valued one-step estimator for the kernel covariance operator between covariates and residuals that enables semiparametrically efficient inference on noise heterogeneity and residual independence in additive noise models.