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 2020-05-01 to 2024-05-01.)

Article	Citations
Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score	337
Nonparametric regression using deep neural networks with ReLU activation function	104
Surprises in high-dimensional ridgeless least squares interpolation	78
Predictive inference with the jackknife+	77
Analytical nonlinear shrinkage of large-dimensional covariance matrices	72
Entrywise eigenvector analysis of random matrices with low expected rank	68
The hardness of conditional independence testing and the generalised covariance measure	60
E-values: Calibration, combination and applications	52
Just interpolate: Kernel “Ridgeless” regression can generalize	51
Robust inference with knockoffs	47
Learning models with uniform performance via distributionally robust optimization	44
Time-uniform, nonparametric, nonasymptotic confidence sequences	42
On the rate of convergence of fully connected deep neural network regression estimates	34
Partial identifiability of restricted latent class models	34
A simple measure of conditional dependence	34
Average treatment effects in the presence of unknown interference	33
Distribution and quantile functions, ranks and signs in dimension d: A measure transportation approach	33
Debiased inverse-variance weighted estimator in two-sample summary-data Mendelian randomization	31
Robust multivariate nonparametric tests via projection averaging	30
Limiting laws for divergent spiked eigenvalues and largest nonspiked eigenvalue of sample covariance matrices	27
A shrinkage principle for heavy-tailed data: High-dimensional robust low-rank matrix recovery	26
Post hoc confidence bounds on false positives using reference families	25
Convergence rates of variational posterior distributions	25
Linearized two-layers neural networks in high dimension	24
Robust multivariate mean estimation: The optimality of trimmed mean	24

A general approach for cure models in survival analysis	24
Foundations of structural causal models with cycles and latent variables	24
Posterior concentration for Bayesian regression trees and forests	23
Transfer learning for nonparametric classification: Minimax rate and adaptive classifier	23
Approximate Message Passing algorithms for rotationally invariant matrices	22
On cross-validated Lasso in high dimensions	22
Conformal prediction beyond exchangeability	21
Permutation methods for factor analysis and PCA	21
Classification accuracy as a proxy for two-sample testing	20
Simultaneous high-probability bounds on the false discovery proportion in structured, regression and online settings	20
Singular vector and singular subspace distribution for the matrix denoising model	20
Spiked separable covariance matrices and principal components	20
Controlled sequential Monte Carlo	20
Testing for stationarity of functional time series in the frequency domain	20
Which bridge estimator is the best for variable selection?	19
The distribution of the Lasso: Uniform control over sparse balls and adaptive parameter tuning	19
Concentration of tempered posteriors and of their variational approximations	19
A framework for adaptive MCMC targeting multimodal distributions	19
Nonparametric statistical inference for drift vector fields of multi-dimensional diffusions	19
Extreme conditional expectile estimation in heavy-tailed heteroscedastic regression models	19
Estimation of low-rank matrices via approximate message passing	19
Nonparametric drift estimation for i.i.d. paths of stochastic differential equations	19
Only closed testing procedures are admissible for controlling false discovery proportions	18
Optimal change point detection and localization in sparse dynamic networks	18
Distance-based and RKHS-based dependence metrics in high dimension	18
Optimal estimation of Gaussian mixtures via denoised method of moments	18
Distributed linear regression by averaging	18
The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy	17
Bridging the gap between constant step size stochastic gradient descent and Markov chains	17
A general framework for Bayes structured linear models	17
Empirical process results for exchangeable arrays	17
Optimality of spectral clustering in the Gaussian mixture model	17
Local uncertainty sampling for large-scale multiclass logistic regression	16
Isotropic covariance functions on graphs and their edges	16
Optimal rates of entropy estimation over Lipschitz balls	16
Adaptive transfer learning	16
Asymptotically independent U-statistics in high-dimensional testing	16
Fréchet change-point detection	15
Geometrizing rates of convergence under local differential privacy constraints	15
Minimax rates in sparse, high-dimensional change point detection	15
Relaxing the assumptions of knockoffs by conditioning	15
Test of significance for high-dimensional longitudinal data	15
Beyond Gaussian approximation: Bootstrap for maxima of sums of independent random vectors	15
High-dimensional consistent independence testing with maxima of rank correlations	14
Large sample properties of partitioning-based series estimators	14
Segmentation and estimation of change-point models: False positive control and confidence regions	14
Construction of mixed orthogonal arrays with high strength	14
Valid post-selection inference in model-free linear regression	14
On the optimal reconstruction of partially observed functional data	14
Causal discovery in heavy-tailed models	13

Testing in high-dimensional spiked models	13
Theoretical and computational guarantees of mean field variational inference for community detection	13
Factor-driven two-regime regression	13
Optimal adaptivity of signed-polygon statistics for network testing	13
Conditional calibration for false discovery rate control under dependence	13
Bridging convex and nonconvex optimization in robust PCA: Noise, outliers and missing data	13
Asymptotic distributions of high-dimensional distance correlation inference	13
Augmented minimax linear estimation	13
Semiparametric optimal estimation with nonignorable nonresponse data	13
Testing for outliers with conformal p-values	13
Rejoinder: “Nonparametric regression using deep neural networks with ReLU activation function”	13
Two-sample hypothesis testing for inhomogeneous random graphs	12
Testing community structure for hypergraphs	12
Hypothesis testing for high-dimensional time series via self-normalization	12
Improved central limit theorem and bootstrap approximations in high dimensions	12
Singularity, misspecification and the convergence rate of EM	12
Additive regression with Hilbertian responses	12
Statistically optimal and computationally efficient low rank tensor completion from noisy entries	12
Subspace estimation from unbalanced and incomplete data matrices: ℓ2,∞ statistical guarantees	12
Peskun–Tierney ordering for Markovian Monte Carlo: Beyond the reversible scenario	12
Convergence of covariance and spectral density estimates for high-dimensional locally stationary processes	11
Heteroskedastic PCA: Algorithm, optimality, and applications	11
On spike and slab empirical Bayes multiple testing	11
Adaptive test of independence based on HSIC measures	11
Robust covariance estimation under $L_{4}-L_{2}$ norm equivalence	11
Distribution and correlation-free two-sample test of high-dimensional means	11
Inference for change points in high-dimensional data via selfnormalization	11
Distributed statistical inference for massive data	11
Local nearest neighbour classification with applications to semi-supervised learning	11
An optimal statistical and computational framework for generalized tensor estimation	11
Community detection on mixture multilayer networks via regularized tensor decomposition	11
Is infinity that far? A Bayesian nonparametric perspective of finite mixture models	11
Extending the validity of frequency domain bootstrap methods to general stationary processes	11
Robust inference via multiplier bootstrap	11
Coupled conditional backward sampling particle filter	11
Density deconvolution under general assumptions on the distribution of measurement errors	10
Minimax estimation of smooth optimal transport maps	10
Minimax optimal rates for Mondrian trees and forests	10
Random graph asymptotics for treatment effect estimation under network interference	10
Measuring dependence in the Wasserstein distance for Bayesian nonparametric models	10
Minimax optimal conditional independence testing	10
LASSO-driven inference in time and space	10
Monitoring for a change point in a sequence of distributions	10
Sharp instruments for classifying compliers and generalizing causal effects	10
Empirical Bayes oracle uncertainty quantification for regression	10
Estimation of the number of components of nonparametric multivariate finite mixture models	10
Semiparametric Bayesian causal inference	10
An adaptable generalization of Hotelling’s $T^{2}$ test in high dimension	10
Some theoretical properties of GANS	9
Inference for spherical location under high concentration	9
On universally consistent and fully distribution-free rank tests of vector independence	9
Wasserstein $F$-tests and confidence bands for the Fréchet regression of density response curves	9
A precise high-dimensional asymptotic theory for boosting and minimum-ℓ1-norm interpolated classifiers	9
Second-order Stein: SURE for SURE and other applications in high-dimensional inference	9
Adaptive estimation in structured factor models with applications to overlapping clustering	9
Orthogonal statistical learning	9
Optimal rates for independence testing via U-statistic permutation tests	9
Analysis of a two-layer neural network via displacement convexity	9
Isotonic regression in multi-dimensional spaces and graphs	9
Minimax optimality of permutation tests	9