Proposes a new estimator for β0 in the partial linear model that attains rate n^{-1/2} + δ^a_μ + (δ^s_μ)^2 with matching lower bound, eliminating first-order stochastic nuisance error.
H., Dukes, O., & Balakrishnan, S
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Develops higher-order influence function estimators for implicitly defined parameters in non-separable structural models using U-processes theory.
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Optimally taming biases in black-box models for efficient semiparametric estimation
Proposes a new estimator for β0 in the partial linear model that attains rate n^{-1/2} + δ^a_μ + (δ^s_μ)^2 with matching lower bound, eliminating first-order stochastic nuisance error.