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.
It’s hard to be normal: The impact of noise on structure-agnostic estimation.arXiv preprint arXiv:2507.02275,
2 Pith papers cite this work. Polarity classification is still indexing.
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FinInvest-GTCN combines graph, temporal, and causal networks with meta-causal adaptation to improve risk-adjusted predictions for VC investments, achieving RA-MSE of 2.51 and 18.7% higher simulated returns on proprietary data.
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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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FinInvest-GTCN: Explainable Graph-Temporal-Causal Modeling for Risk-Aware Investment Decision Optimization
FinInvest-GTCN combines graph, temporal, and causal networks with meta-causal adaptation to improve risk-adjusted predictions for VC investments, achieving RA-MSE of 2.51 and 18.7% higher simulated returns on proprietary data.