OOIR: Observatory of International Research

Papers

(The TQCC of Annals of Statistics is 9. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-08-01 to 2024-08-01.)

Article	Citations
Nonparametric regression using deep neural networks with ReLU activation function	107
Surprises in high-dimensional ridgeless least squares interpolation	88
Predictive inference with the jackknife+	86
Analytical nonlinear shrinkage of large-dimensional covariance matrices	83
E-values: Calibration, combination and applications	57
Learning models with uniform performance via distributionally robust optimization	46
Time-uniform, nonparametric, nonasymptotic confidence sequences	44
On the rate of convergence of fully connected deep neural network regression estimates	43
Distribution and quantile functions, ranks and signs in dimension d: A measure transportation approach	42
A simple measure of conditional dependence	40
Debiased inverse-variance weighted estimator in two-sample summary-data Mendelian randomization	37
Average treatment effects in the presence of unknown interference	36
Partial identifiability of restricted latent class models	35
Robust multivariate nonparametric tests via projection averaging	33
Transfer learning for nonparametric classification: Minimax rate and adaptive classifier	29
Foundations of structural causal models with cycles and latent variables	29
A shrinkage principle for heavy-tailed data: High-dimensional robust low-rank matrix recovery	28
Robust multivariate mean estimation: The optimality of trimmed mean	27
Convergence rates of variational posterior distributions	27
Posterior concentration for Bayesian regression trees and forests	26
A general approach for cure models in survival analysis	25
Linearized two-layers neural networks in high dimension	25
Conformal prediction beyond exchangeability	25
The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy	24
On cross-validated Lasso in high dimensions	24

Approximate Message Passing algorithms for rotationally invariant matrices	23
Testing for stationarity of functional time series in the frequency domain	22
Adaptive transfer learning	22
The distribution of the Lasso: Uniform control over sparse balls and adaptive parameter tuning	22
Extreme conditional expectile estimation in heavy-tailed heteroscedastic regression models	22
Distance-based and RKHS-based dependence metrics in high dimension	22
Only closed testing procedures are admissible for controlling false discovery proportions	21
Singular vector and singular subspace distribution for the matrix denoising model	21
Simultaneous high-probability bounds on the false discovery proportion in structured, regression and online settings	21
Classification accuracy as a proxy for two-sample testing	21
Permutation methods for factor analysis and PCA	21
Controlled sequential Monte Carlo	20
Asymptotically independent U-statistics in high-dimensional testing	20
Spiked separable covariance matrices and principal components	20
Minimax rates in sparse, high-dimensional change point detection	20
Optimal estimation of Gaussian mixtures via denoised method of moments	20
Estimation of low-rank matrices via approximate message passing	20
Nonparametric drift estimation for i.i.d. paths of stochastic differential equations	20
Which bridge estimator is the best for variable selection?	19
A framework for adaptive MCMC targeting multimodal distributions	19
Optimal change point detection and localization in sparse dynamic networks	19
Fréchet change-point detection	18
Improved central limit theorem and bootstrap approximations in high dimensions	18
Distributed linear regression by averaging	18
Geometrizing rates of convergence under local differential privacy constraints	18
Test of significance for high-dimensional longitudinal data	18
Empirical process results for exchangeable arrays	18
Isotropic covariance functions on graphs and their edges	18
Beyond Gaussian approximation: Bootstrap for maxima of sums of independent random vectors	17
High-dimensional consistent independence testing with maxima of rank correlations	17
Optimality of spectral clustering in the Gaussian mixture model	17
A general framework for Bayes structured linear models	17
Bridging convex and nonconvex optimization in robust PCA: Noise, outliers and missing data	16
Relaxing the assumptions of knockoffs by conditioning	16
Construction of mixed orthogonal arrays with high strength	16
Optimal rates of entropy estimation over Lipschitz balls	16
Singularity, misspecification and the convergence rate of EM	15
Causal discovery in heavy-tailed models	15
Testing for outliers with conformal p-values	15
Community detection on mixture multilayer networks via regularized tensor decomposition	15
Asymptotic distributions of high-dimensional distance correlation inference	15
Valid post-selection inference in model-free linear regression	14
Statistical guarantees for Bayesian uncertainty quantification in nonlinear inverse problems with Gaussian process priors	14
An optimal statistical and computational framework for generalized tensor estimation	14
Inference for change points in high-dimensional data via selfnormalization	14
Theoretical and computational guarantees of mean field variational inference for community detection	14
Augmented minimax linear estimation	14
Additive regression with Hilbertian responses	14
Peskun–Tierney ordering for Markovian Monte Carlo: Beyond the reversible scenario	14
Convergence of covariance and spectral density estimates for high-dimensional locally stationary processes	13

Conditional calibration for false discovery rate control under dependence	13
Distributed statistical inference for massive data	13
Rejoinder: “Nonparametric regression using deep neural networks with ReLU activation function”	13
Deep learning for the partially linear Cox model	13
Optimal adaptivity of signed-polygon statistics for network testing	13
Heteroskedastic PCA: Algorithm, optimality, and applications	13
Hypothesis testing for high-dimensional time series via self-normalization	13
Factor-driven two-regime regression	13
Testing community structure for hypergraphs	13
Semiparametric optimal estimation with nonignorable nonresponse data	13
Subspace estimation from unbalanced and incomplete data matrices: ℓ2,∞ statistical guarantees	13
Two-sample hypothesis testing for inhomogeneous random graphs	13
Statistically optimal and computationally efficient low rank tensor completion from noisy entries	13
Minimax estimation of smooth optimal transport maps	12
On spike and slab empirical Bayes multiple testing	12
Empirical Bayes oracle uncertainty quantification for regression	12
LASSO-driven inference in time and space	12
Density deconvolution under general assumptions on the distribution of measurement errors	12
Is infinity that far? A Bayesian nonparametric perspective of finite mixture models	12
On universally consistent and fully distribution-free rank tests of vector independence	12
Estimation of the number of components of nonparametric multivariate finite mixture models	11
Additive regression for non-Euclidean responses and predictors	11
On extended admissible procedures and their nonstandard Bayes risk	11
Orthogonal statistical learning	11
A precise high-dimensional asymptotic theory for boosting and minimum-ℓ1-norm interpolated classifiers	11
Minimax optimal rates for Mondrian trees and forests	11
Adaptive test of independence based on HSIC measures	11
Wasserstein $F$-tests and confidence bands for the Fréchet regression of density response curves	11
Powerful knockoffs via minimizing reconstructability	11
Second-order Stein: SURE for SURE and other applications in high-dimensional inference	11
Extending the validity of frequency domain bootstrap methods to general stationary processes	11
Coupled conditional backward sampling particle filter	11
Semiparametric Bayesian causal inference	10
Random graph asymptotics for treatment effect estimation under network interference	10
Sharp instruments for classifying compliers and generalizing causal effects	10
Consistent nonparametric estimation for heavy-tailed sparse graphs	10
Robust sub-Gaussian estimation of a mean vector in nearly linear time	10
Analysis of a two-layer neural network via displacement convexity	10
Computational barriers to estimation from low-degree polynomials	10
Isotonic regression in multi-dimensional spaces and graphs	10
Marginal singularity and the benefits of labels in covariate-shift	10
Multivariate ranks and quantiles using optimal transport: Consistency, rates and nonparametric testing	10
Monitoring for a change point in a sequence of distributions	10
Community detection with dependent connectivity	10
The interpolation phase transition in neural networks: Memorization and generalization under lazy training	10
Measuring dependence in the Wasserstein distance for Bayesian nonparametric models	10
Minimax optimal conditional independence testing	10
Optimal rates for independence testing via U-statistic permutation tests	9
Necessary and sufficient conditions for variable selection consistency of the LASSO in high dimensions	9
Adaptive estimation in structured factor models with applications to overlapping clustering	9
Fundamental barriers to high-dimensional regression with convex penalties	9
Partial recovery for top-k ranking: Optimality of MLE and SubOptimality of the spectral method	9
Inference for spherical location under high concentration	9
Minimax optimality of permutation tests	9
Central limit theorem and bootstrap approximation in high dimensions: Near 1/n rates via implicit smoothing	9
Multiple block sizes and overlapping blocks for multivariate time series extremes	9
Admissible ways of merging p-values under arbitrary dependence	9