OOIR: Observatory of International Research

Papers

(The TQCC of Annals of Statistics is 8. 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 2020-11-01 to 2024-11-01.)

Article	Citations
Surprises in high-dimensional ridgeless least squares interpolation	96
Predictive inference with the jackknife+	90
E-values: Calibration, combination and applications	61
Learning models with uniform performance via distributionally robust optimization	50
A simple measure of conditional dependence	48
Time-uniform, nonparametric, nonasymptotic confidence sequences	46
Distribution and quantile functions, ranks and signs in dimension d: A measure transportation approach	46
On the rate of convergence of fully connected deep neural network regression estimates	45
Debiased inverse-variance weighted estimator in two-sample summary-data Mendelian randomization	43
Average treatment effects in the presence of unknown interference	36
Robust multivariate nonparametric tests via projection averaging	35
A shrinkage principle for heavy-tailed data: High-dimensional robust low-rank matrix recovery	34
Foundations of structural causal models with cycles and latent variables	33
Transfer learning for nonparametric classification: Minimax rate and adaptive classifier	32
Conformal prediction beyond exchangeability	31
Robust multivariate mean estimation: The optimality of trimmed mean	28
Approximate Message Passing algorithms for rotationally invariant matrices	27
On cross-validated Lasso in high dimensions	27
Linearized two-layers neural networks in high dimension	27
Estimation of low-rank matrices via approximate message passing	25
The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy	25
Extreme conditional expectile estimation in heavy-tailed heteroscedastic regression models	24
Optimal change point detection and localization in sparse dynamic networks	24
Distance-based and RKHS-based dependence metrics in high dimension	24
The distribution of the Lasso: Uniform control over sparse balls and adaptive parameter tuning	24

Adaptive transfer learning	23
Singular vector and singular subspace distribution for the matrix denoising model	23
Simultaneous high-probability bounds on the false discovery proportion in structured, regression and online settings	21
Spiked separable covariance matrices and principal components	21
Classification accuracy as a proxy for two-sample testing	21
Only closed testing procedures are admissible for controlling false discovery proportions	21
Empirical process results for exchangeable arrays	21
Construction of mixed orthogonal arrays with high strength	20
Asymptotically independent U-statistics in high-dimensional testing	20
Nonparametric drift estimation for i.i.d. paths of stochastic differential equations	20
Bridging convex and nonconvex optimization in robust PCA: Noise, outliers and missing data	20
Minimax rates in sparse, high-dimensional change point detection	20
High-dimensional consistent independence testing with maxima of rank correlations	19
Improved central limit theorem and bootstrap approximations in high dimensions	19
Distributed linear regression by averaging	19
Optimality of spectral clustering in the Gaussian mixture model	19
Optimal rates of entropy estimation over Lipschitz balls	19
Fréchet change-point detection	18
An optimal statistical and computational framework for generalized tensor estimation	18
Minimax estimation of smooth optimal transport maps	17
Beyond Gaussian approximation: Bootstrap for maxima of sums of independent random vectors	17
Causal discovery in heavy-tailed models	17
Testing for outliers with conformal p-values	17
Heteroskedastic PCA: Algorithm, optimality, and applications	16
Community detection on mixture multilayer networks via regularized tensor decomposition	16
Is infinity that far? A Bayesian nonparametric perspective of finite mixture models	16
Singularity, misspecification and the convergence rate of EM	16
Asymptotic distributions of high-dimensional distance correlation inference	16
Conditional calibration for false discovery rate control under dependence	15
Statistically optimal and computationally efficient low rank tensor completion from noisy entries	15
Inference for change points in high-dimensional data via selfnormalization	15
Distributed statistical inference for massive data	15
Optimal adaptivity of signed-polygon statistics for network testing	14
Multivariate ranks and quantiles using optimal transport: Consistency, rates and nonparametric testing	14
Statistical guarantees for Bayesian uncertainty quantification in nonlinear inverse problems with Gaussian process priors	14
Factor-driven two-regime regression	14
Testing community structure for hypergraphs	14
Subspace estimation from unbalanced and incomplete data matrices: ℓ2,∞ statistical guarantees	14
Density deconvolution under general assumptions on the distribution of measurement errors	14
On universally consistent and fully distribution-free rank tests of vector independence	14
Augmented minimax linear estimation	14
Semiparametric optimal estimation with nonignorable nonresponse data	14
Peskun–Tierney ordering for Markovian Monte Carlo: Beyond the reversible scenario	14
Adaptive test of independence based on HSIC measures	14
Empirical Bayes oracle uncertainty quantification for regression	13
Deep learning for the partially linear Cox model	13
Convergence of covariance and spectral density estimates for high-dimensional locally stationary processes	13
Covariance estimation under one-bit quantization	12
Optimal rates for independence testing via U-statistic permutation tests	12
Statistical inference for principal components of spiked covariance matrices	12

Minimax optimal conditional independence testing	12
Powerful knockoffs via minimizing reconstructability	12
Marginal singularity and the benefits of labels in covariate-shift	12
LASSO-driven inference in time and space	12
On extended admissible procedures and their nonstandard Bayes risk	12
Second-order Stein: SURE for SURE and other applications in high-dimensional inference	11
Measuring dependence in the Wasserstein distance for Bayesian nonparametric models	11
Estimation of the number of components of nonparametric multivariate finite mixture models	11
A precise high-dimensional asymptotic theory for boosting and minimum-ℓ1-norm interpolated classifiers	11
Random graph asymptotics for treatment effect estimation under network interference	11
Wasserstein $F$-tests and confidence bands for the Fréchet regression of density response curves	11
Additive regression for non-Euclidean responses and predictors	11
The interpolation phase transition in neural networks: Memorization and generalization under lazy training	11
Orthogonal statistical learning	11
Robust sub-Gaussian estimation of a mean vector in nearly linear time	11
Fundamental barriers to high-dimensional regression with convex penalties	10
Monitoring for a change point in a sequence of distributions	10
Computational barriers to estimation from low-degree polynomials	10
Minimax optimality of permutation tests	10
Partial recovery for top-k ranking: Optimality of MLE and SubOptimality of the spectral method	10
Central limit theorem for linear spectral statistics of large dimensional Kendall’s rank correlation matrices and its applications	10
Admissible ways of merging p-values under arbitrary dependence	10
Consistent nonparametric estimation for heavy-tailed sparse graphs	10
Analysis of a two-layer neural network via displacement convexity	10
Isotonic regression in multi-dimensional spaces and graphs	10
Community detection with dependent connectivity	10
Central limit theorem and bootstrap approximation in high dimensions: Near 1/n rates via implicit smoothing	9
Max-sum tests for cross-sectional independence of high-dimensional panel data	9
Optimal difference-based variance estimators in time series: A general framework	9
Estimating the number of components in finite mixture models via the Group-Sort-Fuse procedure	9
Necessary and sufficient conditions for variable selection consistency of the LASSO in high dimensions	9
Precise statistical analysis of classification accuracies for adversarial training	9
Multivariate extensions of isotonic regression and total variation denoising via entire monotonicity and Hardy–Krause variation	9
The Lasso with general Gaussian designs with applications to hypothesis testing	9
Multiple block sizes and overlapping blocks for multivariate time series extremes	9
Optimal estimation of variance in nonparametric regression with random design	8
Strong selection consistency of Bayesian vector autoregressive models based on a pseudo-likelihood approach	8
Uniform convergence of local Fréchet regression with applications to locating extrema and time warping for metric space valued trajectories	8
Continuous-time targeted minimum loss-based estimation of intervention-specific mean outcomes	8
Survival analysis via hierarchically dependent mixture hazards	8
Set structured global empirical risk minimizers are rate optimal in general dimensions	8
Integrative methods for post-selection inference under convex constraints	8
On fixed-domain asymptotics, parameter estimation and isotropic Gaussian random fields with Matérn covariance functions	8
An ℓp theory of PCA and spectral clustering	8
Asymptotic optimality in stochastic optimization	8
Asymptotics for spherical functional autoregressions	8
Approximate and exact designs for total effects	8