OOIR: Observatory of International Research

Papers

(The TQCC of Journal of the Royal Statistical Society Series B-Statistical Methodol is 4. 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 2021-06-01 to 2025-06-01.)

Article	Citations
Mark Pilling's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng	113
Strategic two-sample test via the two-armed bandit process	76
Authors’ reply to the Discussion of ‘From denoising diffusions to denoising Markov models’ at the Discussion Meeting on ‘Probabilistic and statistical aspects of machine learning’	61
Seconder of the vote of thanks to Evans and Didelez and contribution to the Discussion of ‘Parameterizing and simulating from causal models’	54
Catch me if you can: signal localization with knockoff e-values	40
Correlation adjusted debiased Lasso: debiasing the Lasso with inaccurate covariate model	40
Stefano Rizzelli’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen	39
Maozai Tian, Keming Yu and Jiangfeng Wang’s contribution to the Discussion of ‘Safe testing’ by Grünwald, De Heide, and Koolen	38
Image response regression via deep neural networks	35
On Functional Processes with Multiple Discontinuities	34
Safe testing	33
Yinqiu He, Yuqi Gu and Zhilian Ying's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng	32
Issue Information	28
Corrected generalized cross-validation for finite ensembles of penalized estimators	27
SymmPI: predictive inference for data with group symmetries	26
Isadora Antoniano Villalobos's contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes and Walker	22
Proximal survival analysis to handle dependent right censoring	22
Covariate adjustment in multiarmed, possibly factorial experiments	21
Computationally efficient and data-adaptive changepoint inference in high dimension	21
Statistical testing under distributional shifts	21
Adaptive bootstrap tests for composite null hypotheses in the mediation pathway analysis	21
Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods	20
Synthetic Controls with Staggered Adoption	20
Ramses Mena Chavez's contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes and Walker	20
Rungang Han and Anru R. Zhangs contribution to the Discussion of ‘Vintage factor analysis with varimax performs statistical inference’ by Rohe & Zeng	19

Glenn Shafer’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen	18
	18
Robust model averaging prediction of longitudinal response with ultrahigh-dimensional covariates	18
Randomisation inference beyond the sharp null: bounded null hypotheses and quantiles of individual treatment effects	17
Conformal prediction with local weights: randomization enables robust guarantees	17
Strong oracle guarantees for partial penalized tests of high-dimensional generalized linear models	17
Ying Zhou and Xinyi Zhang's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng	16
Estimating the efficiency gain of covariate-adjusted analyses in future clinical trials using external data	16
Proposer of the vote of thanks to Waudy-Smith and Ramdas and contribution to the Discussion of ‘Estimating means of bounded random variables by betting’	15
Pierre-Aurelien Gilliot, Christophe Andrieu, Anthony Lee, Song Liu, and Michael Whitehouse’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machi	15
Authors’ reply to the Discussion of ‘Automatic change-point detection in time series via deep learning’ at the Discussion Meeting on ‘Probabilistic and statistical aspects of machine learning’	15
Testing many constraints in possibly irregular models using incomplete U-statistics	14
Conformalized survival analysis	14
Transfer Learning for High-Dimensional Linear Regression: Prediction, Estimation and Minimax Optimality	14
Graphical criteria for the identification of marginal causal effects in continuous-time survival and event-history analyses	14
Bayesian Context Trees: Modelling and Exact Inference for Discrete Time Series	13
Thomas S. Richardson’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes	13
Andrej Srakar’s contribution to the Discussion of ‘Root and community inference on the latent growth process of a network’ by Crane and Xu	12
Broadcasted nonparametric tensor regression	12
Correction to: Semi-supervised approaches to efficient evaluation of model prediction performance	12
Estimating heterogeneous treatment effects with right-censored data via causal survival forests	12
Empirical Bayes PCA in High Dimensions	12
Hernando Ombao’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’	12
Cluster extent inference revisited: quantification and localisation of brain activity	12
Graph Based Gaussian Processes on Restricted Domains	11
Engression: extrapolation through the lens of distributional regression	11
Ivor Cribben and Anastasiou Andreas’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’	10
J. Goseling and M.N.M. van Lieshout's Contribution to the Discussion of ‘Gaussian Differential Privacy’ by Donget al.	10
Bertrand Clarke's contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes and Walker	10
Seconder of the Vote of Thanks to Donget al.and Contribution to the Discussion of ‘Gaussian Differential Privacy’	10
Identification and estimation of causal peer effects using double negative controls for unmeasured network confounding	10
Tyler J. VanderWeele's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng	10
Filippo Ascolani, Antonio Lijoi and Igor Prünster’s contribution to the Discussion of ‘Root and community inference on the latent growth process of a network’ by Crane and Xu	9
A general framework for cutting feedback within modularized Bayesian inference	9
Gradient synchronization for multivariate functional data, with application to brain connectivity	9
Anthony C Davison and Igor Rodionov’s contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas	9
Scalable couplings for the random walk Metropolis algorithm	9
Kuldeep Kumar's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng	9
Authors’ Reply to the Discussion of ‘Gaussian Differential Privacy’ by Donget al.	9
Efficient Manifold Approximation with Spherelets	9
Martin Larsson and Johannes Ruf’s contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas	8
Adaptive functional principal components analysis	8
Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data Analysis	8
Two-Sample Inference for High-Dimensional Markov Networks	8
Issue Information	8
Universal Prediction Band via Semi-Definite Programming	8
Autoregressive optimal transport models	8
Normalised latent measure factor models	8
Ryan Martin’s contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas	8
Oliver Hines and Karla Diaz-Ordazʼs Contribution to the Discussion of ‘Assumption-Lean Inference For Generalised Linear Model Parameters’ by Vansteelandt and Dukes	8

On the instrumental variable estimation with many weak and invalid instruments	8
Zihao Wen and David L. Dowe’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen	8
Convexity and measures of statistical association	7
Yongmiao Hong, Oliver Linton, Jiajing Sun, and Meiting Zhu’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’	7
Seconder of the vote of thanks to Rohe & Zeng and contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’	7
Peter Krusche and Frank Bretz's Contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al.	7
Kuldeep Kumar’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes	7
Correction to: Ordering factorial experiments	7
Ordering factorial experiments	7
Proposers of the vote of thanks to Crane and Xu and contribution to the Discussion of ‘Root and community inference on the latent growth process of a network’	7
Erratum: Usable and precise asymptotics for generalized linear mixed model analysis and design	7
Yudong Chen and Yining Chen’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’	7
Thorsten Dickhaus’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen	7
Analysis of Networks via the Sparseβ-model	7
α-separability and adjustable combination of amplitude and phase model for functional data	7
Model-assisted sensitivity analysis for treatment effects under unmeasured confounding via regularized calibrated estimation	7
Issue Information	7
Marta Catalano, Augusto Fasano, Matteo Giordano, and Giovanni Rebaudo’s contribution to the Discussion of ‘Root and community inference on the latent growth process of a network’ by Crane and Xu	7
Andrej Srakar’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen	6
Multi-resolution subsampling for linear classification with massive data	6
Correction to: Holdout predictive checks for Bayesian model criticism	6
Priyantha Wijayatunga’s contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes, and Walker	6
Semi-parametric tensor factor analysis by iteratively projected singular value decomposition	5
Sparse Kronecker product decomposition: a general framework of signal region detection in image regression	5
Graphical methods for Order-of-Addition experiments	5
Analytic natural gradient updates for Cholesky factor in Gaussian variational approximation	5
Estimating means of bounded random variables by betting	5
Peng Ding’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes	5
CovNet: Covariance Networks for Functional Data on Multidimensional Domains	5
Jiaqi Gu and Guosheng Yin’s contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes and Walker	5
Ensemble methods for testing a global null	5
Derandomised knockoffs: leveraging e-values for false discovery rate control	5
Contents of Volume 84, 2022	4
Yunxiao Chen and Gongjun Xu's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng	4
Trace-class Gaussian priors for Bayesian learning of neural networks with MCMC	4
Niwen Zhou and Xu Guo’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes	4
Spherical random projection	4
Shakeel Gavioli-Akilagun’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’	4
Inference of Heterogeneous Treatment Effects using Observational Data with High-Dimensional Covariates	4
Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer’s disease	4
Stratification pattern enumerator and its applications	4
Steven R Howard's contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas	4
Optimal Statistical Inference for Individualized Treatment Effects in High-Dimensional Models	4
Randomized empirical likelihood test for ultra-high dimensional means under general covariances	4
Testing homogeneity: the trouble with sparse functional data	4
Debiased inference for a covariate-adjusted regression function	4
Yang Liu's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng	4
A fast asynchronous Markov chain Monte Carlo sampler for sparse Bayesian inference	4