The paper develops methods to estimate the high-dimensional efficient frontier without distributional assumptions.
problem Estimating the mean-variance efficient frontier in high-dimensional settings.
method Random matrix theory and asymptotic analysis for high-dimensional data.
result Developed consistent estimators for the mean, variance, and covariance of the efficient frontier.
Study on conditions for achieving optimal robustness in statistical estimators.
problem Achieving the optimal robustness of estimators in statistical models.
method Developed a Wasserstein analogue of the Cramer-Rao inequality and investigated conditions for achieving the Wasserstein-Cramer-Rao lower bound.
result Conditions for the existence of asymptotically efficient estimators in one-parameter models and location-scale families.
New data improves market impact estimation methods.
problem Improving efficiency of market impact estimation.
method Investigates the use of price trajectory data for market impact estimation.
result Estimation methods using early trade prices outperform established methods asymptotically.
Efficiently estimates models with many variables using minimal communication.
problem Estimating models with a growing number of variables efficiently.
method Two rounds of communication to achieve asymptotically efficient estimator.
result Asymptotically efficient estimator for large-scale distributed data.
Efficient estimator for two-sample functionals improves on oracle performance.
problem Estimating two-sample integral functionals efficiently.
method Weighted nearest neighbour estimator, central limit theorem.
result The estimator can outperform the oracle in certain cases.
Prediction-powered causal inference achieves smaller asymptotic variance than traditional methods.
problem Estimating causal and structural parameters in a semi-supervised setting.
method Combining efficient influence function with debiased machine learning and semi-supervised Riesz regression.
result Asymptotic variances of estimators match the derived efficiency bound.
Paper proposes CIV estimator for categorical instruments in small sample settings.
problem Estimation with categorical instruments in settings with few observations per category.
method CIV estimator leveraging regularization assumption for latent categorical variable.
result CIV estimator is asymptotically normal, efficient, and semiparametrically efficient under homoskedasticity.
Independent component analysis (ICA) has been widely used for blind source separation in many fields such as brain imaging analysis, signal processing and telecommunication. Many statistical techniques based on M-estimates have been proposed for estimating the mixing matrix. Recently, several nonparametric methods have…
New method balances covariates for stable causal survival effect estimation.
problem Estimating causal survival effects in data with conditionally-independent censoring.
method Covariate-balancing approach to empirically stable and asymptotically efficient estimation.
result Validated theoretical results in synthetic and semi-synthetic data.
This paper shows how to carry out efficient asymptotic variance reduction when estimating volatility in the presence of stochastic volatility and microstructure noise with the realized kernels (RK) from [Barndorff-Nielsen et al., 2008] and the quasi-maximum likelihood estimator (QMLE) studied in [Xiu, 2010]. To obtain …
A new method improves stochastic gradient descent for faster and more efficient estimation.
problem Efficient and fast parametric estimation methods.
method Projected stochastic gradient descent corrected by Fisher scoring.
result The method is faster and more efficient than traditional methods.
This study optimizes covariate density and propensity score for efficient ATE estimation.
problem Efficiently estimating average treatment effects (ATEs) with minimal variance.
method Adaptive experiment optimizing both covariate density and propensity score.
result Proposed method minimizes the semiparametric efficiency bound for ATE estimation.
Standard maximum likelihood estimation cannot be applied to discrete energy-based models in the general case because the computation of exact model probabilities is intractable. Recent research has seen the proposal of several new estimators designed specifically to overcome this intractability, but virtually nothing i…
Maximum Likelihood Estimators (MLE) has many good properties. For example, the asymptotic variance of MLE solution attains equality of the asymptotic Cram{é}r-Rao lower bound (efficiency bound), which is the minimum possible variance for an unbiased estimator. However, obtaining such MLE solution requires calculating t…
Improved SV estimator for efficient data valuation.
problem Computational inefficiency in Shapley value estimation.
method Group Testing-based SV estimator with improvements.
result Enhanced asymptotic sample complexity and insights into challenges.
A method for efficient statistical inference from online algorithms.
problem Computational constraints in online algorithms make traditional variance estimation difficult.
method HulC method that wraps around online algorithms to produce valid confidence regions.
result The HulC method produces asymptotically valid confidence regions for online algorithms.
The paper proves the consistency and efficiency of a volatility estimator in noisy data.
problem Proving the consistency and efficiency of a volatility estimator in the presence of microstructure noise.
method Proves asymptotic normality using Central Limit Theorem for Fourier spot volatility estimator.
result Proves consistency and asymptotic efficiency of the Fourier spot volatility estimator in noisy data.
The basic model for high-frequency data in finance is considered, where an efficient price process is observed under microstructure noise. It is shown that this nonparametric model is in Le Cam's sense asymptotically equivalent to a Gaussian shift experiment in terms of the square root of the volatility function σ. A…
Optimal convex loss function improves regression coefficient estimation.
problem Asymptotic variance improvement in linear regression estimation.
method Score matching extension for log-concave projection.
result Semiparametric estimator attains minimal asymptotic covariance.
The paper analyzes methods for estimating linear functionals from observational data, proving upper bounds and showing optimal procedures.
problem Estimating linear functionals from observational data in causal inference and bandit literature.
method Two-stage procedures that first estimate treatment effect function, then use it to estimate the linear functional.
result Proves non-asymptotic upper bounds on mean-squared error for two-stage procedures and shows instance-dependent optimality.
Paper proposes a debiased estimator for adaptive linear regression.
problem Non-normal asymptotic behavior of OLS estimator in adaptive linear regression.
method Adaptive linear estimating equations to construct debiased estimator.
result Established asymptotic normality of the debiased estimator.
New method handles missing data using AI for efficient inference.
problem Parameter estimation and inference with blockwise missing data.
method Tractable solution using AI models and semiparametric theory.
result IBM(RAY) and IBM(Adaptive) estimators achieve efficiency gains.
Improved statistical inference for expensive data using machine learning predictions.
problem Statistical inference under adaptive two-phase multiwave sampling with expensive measurements.
method Multiwave Predict-Then-Debias estimator combining proxy information and expensive measurements.
result Valid estimators and confidence intervals for M-estimation under adaptive sampling.
Efficient inference for adaptive data with directional stability condition.
problem Efficient inference on scalar targets after adaptive data collection.
method Introduces directional stability, a weaker condition than i.i.d. data, and shows asymptotic normality and efficiency of estimators.
result Estimators remain asymptotically normal and semiparametrically efficient under directional stability.
New estimator handles covariate shift with closed-form solution and super-efficiency.
problem Handling covariate shift in missing data and causal inference problems.
method Minimum Wasserstein distance estimation framework.
result Closed-form expression and super-efficiency relative to semiparametric efficient estimator.
Paper proposes a new DR estimator for adaptive experiments with improved performance.
problem Improving policy evaluation in adaptive experiments with dependent samples.
method Adaptive-fitting variant of sample-splitting for non-Donsker nuisance estimators.
result Proposed DR estimator shows better performance than other estimators with dependent samples.
Semiparametric method removes bias in functional bilevel gradient estimation.
problem First-order bias in plug-in hypergradient when lower-level problem is nonparametric.
method Semiparametric debiasing theory based on efficient influence function leads to cross-fitted orthogonal hypergradient estimator.
result Asymptotic normality and uniform control over outer parameter established for the estimator.
Improved AutoDML estimator for causal inference using outcome-adapted shared covariate representation.
problem Efficiency in estimating treatment or policy effects in causal inference.
method Outcome-adapted AutoDML estimator that uses a shared covariate representation that is predictive of the outcome but not the Riesz representer.
result Outcome-adapted AutoDML estimator is asymptotically more efficient than baseline AutoDML.
Paper improves risk estimation for extreme events.
problem Estimating extreme risks accurately.
method Modified Bayes risk for expectiles, asymptotic expansions, efficient estimators.
result Asymptotic normality of estimators proved.
Study guarantees convergence of mean shift mode estimation.
problem Ensuring reliable mode estimation in KDE using mean shift.
method Utilizes Łojasiewicz inequality to prove convergence rate.
result Extends convergence guarantees to biweight kernel.
Efficiently learns exponential family distributions with i.i.d. samples.
problem Learning natural parameters of truncated exponential families efficiently.
method Proposes a novel loss function and computationally efficient estimator.
result Achieves optimal sample complexity and asymptotic normality.
Paper improves off-policy evaluation for reinforcement learning with asymptotically efficient estimators.
problem Estimating target policy performance using offline data collected by a different policy.
method Developed a modified marginalized importance sampling (MIS) estimator that achieves asymptotically efficient error bounds.
result Proved that a simple modification to the MIS estimator can achieve a Cramer-Rao lower bound in mean square error.
This paper improves reinforcement learning by estimating return distributions using quantiles.
problem Improving reinforcement learning by estimating return distributions.
method The paper uses quantile-based distributional reinforcement learning to characterize return distributions.
result The quantile-based approach achieves optimal sample efficiency and asymptotic efficiency.
New method improves treatment effect estimation in adaptive experiments with noncompliance.
problem Estimating average treatment effect in adaptive experiments with binary instrumental variable.
method AMRIV estimator that balances outcome noise and compliance variability.
result AMRIV achieves semiparametric efficiency bound and is robust to noncompliance.
This paper improves reinforcement learning by estimating return distributions using quantiles.
problem Improving reinforcement learning by estimating return distributions.
method Quantile-based distributional reinforcement learning, using quantile-projected distributional Bellman equations.
result The quantile-based approach achieves optimal sample efficiency and asymptotic efficiency.
The paper analyzes portfolio credit risk using Archimedean copulas and introduces efficient simulation methods.
problem Analyzing large losses from credit portfolio defaults with Archimedean copulas.
method Derives asymptotic results and develops variance reduction algorithms for Monte Carlo simulations.
result Proposed algorithms significantly enhance classical Monte Carlo methods for estimating portfolio credit risk.
Sharp estimates on 2-step nilpotent Lie groups' metrics and cones.
problem Estimating asymptotic metrics in 2-step nilpotent Lie groups.
method Developed a novel technique to perturb rectifiable curves.
result Every 2-step nilpotent Riemannian Lie group is at bounded distance from its asymptotic cone.
Efficient tensor decomposition for count data models achieves near-optimal multiway analysis.
problem Efficient tensor decomposition for count data models.
method Rank-constrained maximum-likelihood estimator for tensor decomposition.
result Achieves multiway analysis with variance matching Cramér-Rao Lower Bound up to constants and logarithmic factors.
Paper proposes an online estimator for covariance matrix of SGD iterates.
problem Quantifying variability and randomness of SGD-based estimates in online learning.
method Proposes a fully online estimator for covariance matrix of ASGD using SGD iterates.
result Establishes consistency of the online estimator and shows comparable convergence rate to offline methods.
POMBU improves model-based RL's asymptotic performance by estimating and using uncertainty.
problem Model-based reinforcement learning struggles with model errors, leading to suboptimal performance.
method POMBU uses estimated uncertainty to optimize policies conservatively, improving asymptotic performance.
result POMBU outperforms existing methods in sample efficiency and asymptotic performance.
PPAT uses predictions to improve risk estimation in active testing.
problem Exploiting informative predictions from black-box models for efficient risk estimation.
method Combines LURE estimator with prediction-powered control variate.
result PPAT outperforms existing methods in risk estimation and uncertainty quantification.
Efficiently estimates Weingarten maps and curvatures from manifold data.
problem Estimating Weingarten maps and curvatures from manifold data.
method Statistical model for Weingarten map estimation; convergence rate analysis.
result Convergence rate of the estimator as sample size increases.
Paper shows robust estimators converge to true risk minimizers at optimal rates.
problem Understanding asymptotic properties of robust risk minimizers.
method Investigates robust analogues of empirical risk minimization, focusing on median of means estimator.
result Robust minimizers converge to true minimizers at optimal rates and have similar asymptotic variance.
For massive data, the family of subsampling algorithms is popular to downsize the data volume and reduce computational burden. Existing studies focus on approximating the ordinary least squares estimate in linear regression, where statistical leverage scores are often used to define subsampling probabilities. In this p…
Paper addresses high-dimensional linear regression with missing data, proposing efficient and nearly unbiased estimators.
problem High-dimensional linear regression with blockwise missing covariates and partially observed responses.
method Proposes a computationally efficient estimator and nearly unbiased debiased estimators using blockwise imputation and estimating equations.
result Asymptotically valid confidence intervals and statistical tests constructed based on debiased estimators.
A new method improves robustness and efficiency of Bayesian LOO-CV.
problem Computational expense and unreliability of classical LOO-CV in high-dimensional Bayesian models.
method Proposes a mixture estimator to compute Bayesian LOO-CV criteria with finite asymptotic variance.
result Improved robustness and efficiency in high-dimensional problems.
The paper analyzes logistic regression for rare events data, deriving new insights on estimator efficiency and sampling strategies.
problem Binary logistic regression for rare events data with significantly fewer events than controls.
method Derives asymptotic distribution of MLE, proves under-sampling advantage, and compares over-sampling efficiency.
result Under-sampling a small proportion of nonevents can improve efficiency in rare events data analysis.
New nonparametric estimators improve causal effect estimation.
problem Estimation of causal effects with selection bias.
method Undersmoothing of the highly adaptive lasso for estimating the weighting mechanism.
result Asymptotic efficiency and convergence to nonparametric efficiency bound.