A new kernel-smoothed estimator using complex-domain moment generating functions achieves root-n consistency and asymptotic normality for general linear and nonlinear quantile regression with normal measurement errors in covariates.
Handbook of econometrics , volume=
11 Pith papers cite this work. Polarity classification is still indexing.
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A doubly robust estimator is developed for quantile treatment effects on long-term outcomes by integrating randomized trial data with observational data under surrogate transportability, remaining consistent if either nuisance function is correctly estimated.
Proposes a novel semi-supervised estimator for risk prediction under double censoring that combines limited gold-standard labels with large-scale surrogates, proves theoretical validity, and shows efficiency gains over supervised methods in simulations and a T2D EHR application.
A Neyman-orthogonal estimator paired with Lasso nuisance estimation achieves root-T asymptotic normality for BLP demand parameters under high-dimensional controls and approximate sparsity.
A correctly specified exposure map implies design-side orthogonality conditions, so the exposure radius can be estimated by GMM and tested by overidentification — rejecting the 2 km radius in the GiveDirectly experiment.
PLANE jointly estimates latent gene positions from a target network and proxy embeddings on a larger gene set, with provably optimal channel weighting and demonstrated gains in network recovery and imputation.
A learnable continuous perturbation framework for LLM token prefixes via latent vector transformations, optimized through unbiased estimating equations, yields gains in out-of-domain performance.
A new adaptive variance estimator for relative sparsity coefficients is introduced that fully utilizes the prior asymptotic normality theorem and incorporates variable selection effects.
Develops asymptotic theory and bootstrap inference for the τ-quantile of cross-sectional individual coefficient distributions in panel data under stochastic and deterministic designs.
A model-free estimator for causal effects in two-sample Mendelian randomization that is consistent and asymptotically normal under population heterogeneity between samples.
Predicting question-level rectification difficulty from text and allocating human labels by a square-root rule recovers most of the hybrid human–LLM survey efficiency gains without pilot data.
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Quantile regression with measurement errors
A new kernel-smoothed estimator using complex-domain moment generating functions achieves root-n consistency and asymptotic normality for general linear and nonlinear quantile regression with normal measurement errors in covariates.
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Double/debiased machine learning of quantile treatment effects on long-term outcomes in clinical trials
A doubly robust estimator is developed for quantile treatment effects on long-term outcomes by integrating randomized trial data with observational data under surrogate transportability, remaining consistent if either nuisance function is correctly estimated.
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Semi-supervised Method for Risk Prediction with Doubly Censored EHR Data
Proposes a novel semi-supervised estimator for risk prediction under double censoring that combines limited gold-standard labels with large-scale surrogates, proves theoretical validity, and shows efficiency gains over supervised methods in simulations and a T2D EHR application.
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Estimation of BLP models with high-dimensional controls
A Neyman-orthogonal estimator paired with Lasso nuisance estimation achieves root-T asymptotic normality for BLP demand parameters under high-dimensional controls and approximate sparsity.
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A Design-Based Approach to Testing and Inference in (Quasi-)Experiments with Spillovers
A correctly specified exposure map implies design-side orthogonality conditions, so the exposure radius can be estimated by GMM and tested by overidentification — rejecting the 2 km radius in the GiveDirectly experiment.
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AI-Augmented Statistical Network Estimation with Proxy Gene Embeddings
PLANE jointly estimates latent gene positions from a target network and proxy embeddings on a larger gene set, with provably optimal channel weighting and demonstrated gains in network recovery and imputation.
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Learning Perturbations to Extrapolate Your LLM
A learnable continuous perturbation framework for LLM token prefixes via latent vector transformations, optimized through unbiased estimating equations, yields gains in out-of-domain performance.
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An adaptive variance estimator for relative sparsity
A new adaptive variance estimator for relative sparsity coefficients is introduced that fully utilizes the prior asymptotic normality theorem and incorporates variable selection effects.
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Estimation and Inference for the $\tau$-Quantile of Individual Heterogeneous Coefficient
Develops asymptotic theory and bootstrap inference for the τ-quantile of cross-sectional individual coefficient distributions in panel data under stochastic and deterministic designs.
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A Robust Framework for Two-Sample Mendelian Randomization under Population Heterogeneity
A model-free estimator for causal effects in two-sample Mendelian randomization that is consistent and asymptotically normal under population heterogeneity between samples.
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Rectification Difficulty and Optimal Sample Allocation in LLM-Augmented Surveys
Predicting question-level rectification difficulty from text and allocating human labels by a square-root rule recovers most of the hybrid human–LLM survey efficiency gains without pilot data.