arXiv research

Paper proposes a debiased estimator for adaptive linear regression.

Improved estimator reduces bias in statistical learning models.

New method improves covariance estimation for weighted samples.

The paper analyzes methods for estimating linear functionals from observational data, proving upper bounds and showing optimal procedures.

Extends covariance estimation with multiple targets for better performance.

Proposes online debiasing estimators for adaptive linear regression.

We address challenges in estimating parameters from adaptively collected data.

This work extends Ledoit-Wolf shrinkage to unknown mean covariance estimation.

Proof of Gaussian ML estimator consistency in linear auto-regressive models.

Paper proposes a bias-constrained deep learning approach to non-linear estimation.

The statistical analysis of Randomized Numerical Linear Algebra (RandNLA) algorithms within the past few years has mostly focused on their performance as point estimators. However, this is insufficient for conducting statistical inference, e.g., constructing confidence intervals and hypothesis testing, since the distri…

The paper addresses statistical estimation in MDPs with confounders using instrumental variables.

Spectral methods improve signal recovery in mixed GLMs with precise asymptotics.

Paper addresses high-dimensional linear regression with missing data, proposing efficient and nearly unbiased estimators.

This paper presents the asymptotic behavior of a linear instrumental variables (IV) estimator that uses a ridge regression penalty. The regularization tuning parameter is selected empirically by splitting the observed data into training and test samples. Conditional on the tuning parameter, the training sample creates …

Study asymptotically almost periodic solutions on real hyperbolic manifolds.

The paper provides a non-asymptotic error bound for linear system identification under nonlinear policies.

Study uses deep neural networks for inference in partially linear models with dependent data.

The paper develops a method to create non-asymptotic confidence ellipsoids for linear regression without strong noise distribution assumptions.

The paper analyzes an ensemble of randomly projected linear discriminants for high-dimensional data.

Study GLS estimator properties in multivariate regression with heteroskedastic and autocorrelated errors.

Flexible DNN for survival data, avoiding proportional hazards assumption.

New method for estimating parameters in inverse problems using double robustness.

Paper presents a new framework for covariance matrix estimation with geometric insights.

E$^4$ algorithm optimizes batched linear bandits with minimal regret and batches.

The paper studies the asymptotic behavior of adversarial training under $\ell_\infty$-perturbation.

Corrects mismatch in consistency of nuisance estimators for doubly robust methods.

We provide a pointwise confidence bound for non-linear least-squares with fixed design.

Study on linear regression with dependent covariates, proving universality and error characterization.

This paper consider penalized empirical loss minimization of convex loss functions with unknown non-linear target functions. Using the elastic net penalty we establish a finite sample oracle inequality which bounds the loss of our estimator from above with high probability. If the unknown target is linear this inequali…

Paper establishes limits for accurately estimating low-rank matrices from noisy, non-linear data.

Estimates Markov chain variance efficiently without storing samples.

This paper analyzes divide-and-conquer estimators for functional linear regression without assuming target function in the RKHS.

The growing size of modern data brings many new challenges to existing statistical inference methodologies and theories, and calls for the development of distributed inferential approaches. This paper studies distributed inference for linear support vector machine (SVM) for the binary classification task. Despite a vas…

Proves formula for reconstruction performance in generalized linear models.

Paper addresses identifiability and asymptotics of ODE systems from noisy data.

Paper extends Chernoff sampling for active testing and parameter estimation, improving neural network and regression models.

The paper addresses statistical inference for online decision-making in a contextual bandit setting.

Distributed statistical inference has recently attracted immense attention. The asymptotic efficiency of the maximum likelihood estimator (MLE), the one-step MLE, and the aggregated estimating equation estimator are established for generalized linear models under the "large $n$, diverging $p_n$" framework, where the di…

Develops privacy-preserving methods for longitudinal linear regression.

The paper addresses nonconvex penalized LAD estimation in partial linear models using DNNs.

Optimal downsampling improves GLM performance in imbalanced classification.

New approach estimates treatment effects from decentralized data.

Using stochastic gradient search and the optimal filter derivative, it is possible to perform recursive (i.e., online) maximum likelihood estimation in a non-linear state-space model. As the optimal filter and its derivative are analytically intractable for such a model, they need to be approximated numerically. In [Po…

Improved LDA using a nonlinear covariance estimator for better performance.

Optimal convex loss function improves regression coefficient estimation.

In the setting of high-dimensional linear regression models, we propose two frameworks for constructing pointwise and group confidence sets for penalized estimators which incorporate prior knowledge about the organization of the non-zero coefficients. This is done by desparsifying the estimator as in van de Geer et al.…

Improved statistical inference for expensive data using machine learning predictions.