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.
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This review synthesizes representative advances in high-dimensional statistics, highlights common themes and open problems, and points to key entry works.
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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.
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High-Dimensional Statistics: Reflections on Progress and Open Problems
This review synthesizes representative advances in high-dimensional statistics, highlights common themes and open problems, and points to key entry works.