Paper proposes new density estimators for high-dimensional data.
problem Prohibitive computational cost and slow convergence rate in high-dimensional density estimation.
method Adaptive hyperbolic cross density estimators in mixed smooth Sobolev spaces.
result Proposed estimators do not suffer curse of dimensionality under Integral Probability Metrics.
Machine learning improves high-dimensional matrix estimation.
problem Efficient estimation of high-dimensional matrices.
method Integrates machine learning with classical optimization algorithms for high-dimensional matrix estimation.
result The reparameterized LADMM achieves faster convergence and higher accuracy.
Robust estimators improve high-dimensional data analysis by trimming outliers.
problem Robustifying high-dimensional structured estimation in the presence of outliers and data corruption.
method Trimmed versions of structurally regularized M-estimators, including Least Trimmed Squares and Lasso, are analyzed and optimized.
result General analysis and guarantees for statistical convergence and consistency of trimmed estimators.
Develops a computationally tractable high-dimensional differential privacy estimator.
problem Differential privacy in high dimensions is computationally intractable.
method Combines high-dimensional robust statistics with differential privacy techniques.
result A computationally tractable algorithm with dimension-independent privacy loss.
Estimates high-dimensional posterior densities by marginal distributions and neural networks.
problem High-dimensional probability density estimation for inference is difficult.
method Direct estimation of lower-dimensional marginal distributions, using Moment Networks for fast computation of moments.
result Demonstrates estimation of gravitational wave time series and applications in cosmology.
Improved Sparse Polyak for high-dimensional M-estimation with sparser solutions.
problem High-dimensional M-estimation problems with potential loss of sparsity and accuracy.
method Variant of Sparse Polyak with optimal thresholding operators.
result Retains desirable scaling properties while achieving sparser and more accurate solutions.
This paper reviews nonparametric density estimation methods for high-dimensional data.
problem Challenges in analyzing high-dimensional data with many features.
method Review of nonparametric density estimation algorithms for high-dimensional data.
result Discussion of algorithms and their applications in modal clustering.
Develops methods for estimating and providing confidence bands in sparse high-dimensional additive models.
problem Estimating and providing reliable confidence bands for nonparametric components in high-dimensional additive models.
method Integrates sieve estimation into a high-dimensional Z-estimation framework, employing a multiplier bootstrap procedure.
result Constructs uniformly valid confidence bands for the target component f1 in sparse high-dimensional additive models. A new method for high-dimensional M-estimation with missing outcomes.
problem High-dimensional estimation with missing response data.
method Semi-parametric framework using L1-regularized debiased and doubly robust estimator.
result Established finite sample performance bounds for the DDR estimator.
Study improves Hayashi-Yoshida estimator for high-dimensional stock covolatility.
problem Inconsistent performance of Hayashi-Yoshida estimator in high dimensions.
method Analyzed the limiting spectral distribution of the Hayashi-Yoshida estimator.
result Established the connection between the estimator's spectrum and the true covariance matrix in high dimensions.
High-dimensional data improves treatment effect estimation in randomized experiments.
problem Estimating treatment effects in experiments with many covariates.
method Risk-consistent regression adjustments, cross-estimation, adaptive specification search, machine learning methods.
result High-dimensional regression adjustments can provide valid inference about average treatment effects.
New method estimates treatment effects from high dimensional data.
problem Estimating treatment effects from high dimensional data with confounders.
method Generative modeling approach to backdoor adjustment in variational inference.
result Empirically, estimates interventional likelihood in high dimensional settings.
Paper proposes a new sparsity scheme for high-dimensional VAR models.
problem Estimation of high-dimensional VAR models with sparsity assumptions.
method Regularized estimation procedures for sparse VAR models.
result Threholding extends consistency properties of regularized estimators.
A new particle filter avoids resampling to improve state estimation in high dimensions.
problem Particle deprivation in high-dimensional state spaces.
method A resampling-free particle filter designed to mitigate particle deprivation.
result The filter offers a near-accurate representation of the posterior distribution in high-dimensional contexts.
Survey on efficient robust estimators for high-dimensional statistics.
problem Efficient robust mean estimation in high dimensions.
method Emerging algorithmic techniques in theoretical computer science.
result First efficient robust estimators for fundamental tasks.
Proposes Causal-Batle for estimating treatment effects in small high-dimensional datasets.
problem Estimating treatment effects with small high-dimensional datasets.
method Adopts transfer learning techniques for causal inference.
result Improves treatment effect estimates in small high-dimensional datasets.
FlexCode converts high-dimensional regression to high-dimensional CDE.
problem Complex conditional distributions in high dimensions.
method Reformulates CDE as a non-parametric orthogonal series problem.
result Efficiently estimates conditional densities in high dimensions.
New method for estimating and testing impulse responses in high-dimensional VAR systems.
problem Statistical inference for impulse responses in sparse, high-dimensional vector autoregressions.
method Local projection equations and de-sparsified estimators combined with a non-regularized contemporaneous impact matrix.
result Valid inference procedures for structural impulse responses in high-dimensional systems.
SPPCSO addresses multicollinearity in high-dimensional data, improving model stability and predictive accuracy.
problem Multicollinearity in high-dimensional data leads to unstable estimation and reduced predictive accuracy.
method SPPCSO integrates principal component regression and L1 regularization to adaptively adjust shrinkage factors.
result SPPCSO achieves stable and reliable estimation in high-noise settings, distinguishing signal variables from noise.
Paper proves conditions for estimating precision matrices with Laplacian constraints.
problem Estimating high-dimensional precision matrices with Laplacian constraints.
method Minimizing Stein's loss with conditions on graph connectivity and Laplacian constraints.
result High-dimensional consistency achieved with Laplacian constraints, independent of graph structure.
Empirical Bayes method improves high-dimensional classification accuracy.
problem High-dimensional classification with sparse mean differences.
method Dirichlet process mixture model and variational Bayes algorithm.
result Effective estimation of mean difference leads to reduced misclassification.
hdm package offers methods for high-dimensional metrics estimation and uncertainty quantification.
problem Estimation and uncertainty quantification in high-dimensional sparse models.
method Efficient estimators and uniformly valid confidence intervals for various parameters in high-dimensional regression models.
result The package provides methods for estimating and testing parameters in high-dimensional models, including ATE and ATET.
New method for high-dimensional regression with unknown scale parameter.
problem High-dimensional linear regression with unknown scale parameter.
method Penalized Huber M-estimator with adaptive Lepski's method. result The method effectively calibrates scale in high-dimensional robust regression.
Improved ridge estimators avoid tuning parameters for high-dimensional data.
problem Difficulty in calibrating tuning parameters for ridge estimators.
method Developed modified ridge estimators that eliminate tuning parameters.
result Modified ridge estimators outperform standard methods in prediction accuracy.
A new method reduces high-dimensional state space for dynamic choice models.
problem Estimation of dynamic discrete choice models is computationally intensive and infeasible in high-dimensional settings.
method Recursive partitioning algorithm to reduce dimensionality of high-dimensional state space.
result Our method reduces estimation bias and makes estimation feasible.
The paper establishes a general oracle inequality for high-dimensional prediction.
problem High-dimensional data in modern sciences.
method Penalized estimators like lasso, scaled lasso, square-root lasso, elastic net.
result Generic estimators can provide consistent prediction with any design matrix.
New method for estimating high-dimensional binary time series coefficients.
problem Statistical inference for high-dimensional binary time series.
method Post-selection estimator and second-order wild bootstrap algorithm.
result Good finite-sample performance of the proposed method.
Proposes a new method for high-dimensional density estimation.
problem Estimating high-dimensional probability density functions efficiently.
method Tensorizing flow method combining tensor-train and flow-based generative modeling.
result Efficiently constructs an approximate density in tensor-train form and trains a flow model to match empirical distribution.
Develops inequalities for high-dimensional linear processes with dependent innovations.
problem Estimating high-dimensional VAR(p) systems and HAC covariance estimation.
method Concentration inequalities for l∞ norm of vector linear processes with sub-Weibull, mixingale innovations. result Obtained concentration bounds for the maximum entrywise norm of lag-h autocovariance matrices. Paper derives optimal shrinkage estimator for high-dimensional mean vectors.
problem Estimating high-dimensional mean vectors with shrinkage.
method Linear shrinkage estimator using random matrix theory.
result Optimal shrinkage estimator minimizes quadratic loss asymptotically.
New method estimates treatment effects in high-dimensional survival data.
problem Estimating causal effects for survival outcomes with many covariates.
method Orthogonal score method and Hazards Difference (HDi) estimator for high-dimensional additive hazards model.
result Valid inference for conditional treatment effect under high-dimensional setting.
New algorithms improve privacy in statistical estimation by making them robust.
problem Improving privacy in statistical estimation methods.
method Black-box reduction from privacy to robustness, using Sum-of-Squares method.
result Design of polynomial-time private estimators with optimal tradeoffs among sample complexity, accuracy, and privacy.
Paper explores differential privacy in high-dimensional federated learning, tackling server trustworthiness and estimation.
problem Maintaining privacy in distributed environments with high-dimensional data.
method Investigates scenarios with untrusted and trusted central servers, introduces novel federated estimation algorithms for linear regression models.
result Tight minimax rates depend on high-dimensionality even with sparsity assumptions, and novel algorithms handle slight variations among distributed models.
A new method estimates high-dimensional multi-response models with structured parameters.
problem Learning high-dimensional multi-response linear models with structured parameters.
method Alternating Estimation (AltEst) procedure based on the generalized Dantzig selector.
result Error of AltEst estimates converges linearly to a minimum achievable level with high probability.
We provide a general theory of the expectation-maximization (EM) algorithm for inferring high dimensional latent variable models. In particular, we make two contributions: (i) For parameter estimation, we propose a novel high dimensional EM algorithm which naturally incorporates sparsity structure into parameter estima…
SSNL improves simulation-based inference for high-dimensional data.
problem Performance degradation in neural likelihood estimation for high-dimensional data.
method Surjective Sequential Neural Likelihood (SSNL) using surjective normalizing flow models.
result SSNL avoids manual crafting of summary statistics and outperforms state-of-the-art methods.
Regularized estimators can provide consistent estimates even when identification fails in linear models.
problem Challenges in identifying structural parameters in linear models due to identification failure.
method Regularized estimators, including ridge regularization, gradient descent, and PCA.
result Asymptotic distribution of regularized estimators can be nonstandard.
Paper introduces a new IV regression method for mixed-frequency data.
problem Estimating high-dimensional slope parameters in mixed-frequency data.
method Tikhonov-regularized estimator for high-dimensional linear IV regression.
result High-dimensional slope parameter can be accurately estimated using a low-frequency instrumental variable.
Efficient streaming algorithms for robust statistics with near-optimal memory.
problem High-dimensional robust statistics tasks in streaming model.
method First efficient streaming algorithms with near-optimal memory requirements.
result Near-optimal error guarantees and space complexity nearly-linear in the dimension for robust mean estimation.
New method learns unbiased treatment representations from structured high-dimensional data.
problem Estimating causal effects from high-dimensional, structured treatments.
method Contrastive learning approach to learn unbiased treatment representations.
result The method identifies causal factors and discards non-causal ones, leading to unbiased causal effect estimates.
Unified framework for estimating high-dimensional conditional factor models.
problem Estimating high-dimensional conditional latent factor models with practical limitations.
method Constrained nuclear norm regularization and cross-validation for parameter selection.
result Imposing homogeneity improves model predictability, with new method outperforming alternatives.
R package for high-dimensional metrics estimation and uncertainty quantification.
problem Estimation and uncertainty quantification in high-dimensional sparse models.
method Statistical methods for Lasso regression, joint confidence intervals, significance testing.
result Efficient estimators and uniformly valid confidence intervals for high-dimensional models.
Proposes methods for constructing confidence sets in high-dimensional structured sparsity.
problem Building confidence regions for high-dimensional structured sparsity models.
method Desparsification of the estimator, structured matrix norm penalty, and asymptotic pivot construction.
result Developed methods for constructing asymptotic confidence regions in high-dimensional structured sparsity models.
Efficiently estimates high-dimensional varying index coefficient models without link function estimation.
problem Parameter estimation in high-dimensional varying index coefficient models.
method Stein's identity for computationally efficient estimators of sparse or low-rank parameters.
result Optimal statistical rates of convergence for estimators in both sparse and low-rank settings.
Gradient descent solves robust mean estimation in high dimensions.
problem High-dimensional robust mean estimation in the presence of adversarial outliers.
method Gradient descent with a structural lemma showing near-optimal solutions.
result Gradient descent can solve the robust mean estimation problem directly.
Improved location estimation for high-dimensional data with finite sample size.
problem Estimating the shift in high-dimensional data with limited samples.
method Smoothed estimators and bounds on subgamma vectors.
result Convergence to Cramér-Rao bound for finite sample sizes.
New method implicitly regularizes high-dimensional linear regression using gradient descent.
problem Sparse vector estimation in high-dimensional linear regression.
method Gradient descent on residual sum of squares with early stopping under overparameterization.
result Gradient descent implicitly leads to nearly sparse optimal solutions without explicit penalties.
The paper optimizes hyperplanes for binary classification in high-dimensional data with latent Gaussian mixtures.
problem Binary classification in high-dimensional data with latent Gaussian mixtures.
method Generalized least squares estimator for estimating the direction of the optimal separating hyperplane. Simple correction for intercept estimation.
result The procedure is minimax optimal in many scenarios and can retain the interpolation property.