Journal of the Royal Statistical Society Series B-Statistical Methodol

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 2022-05-01 to 2026-05-01.)
ArticleCitations
Mark Pilling's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng79
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’60
Seconder of the vote of thanks to Evans and Didelez and contribution to the Discussion of ‘Parameterizing and simulating from causal models’59
Stefano Rizzelli’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen58
Maozai Tian, Keming Yu and Jiangfeng Wang’s contribution to the Discussion of ‘Safe testing’ by Grünwald, De Heide, and Koolen43
Strategic two-sample test via the two-armed bandit process43
Correlation adjusted debiased Lasso: debiasing the Lasso with inaccurate covariate model36
Skew-symmetric approximations of posterior distributions32
Catch me if you can: signal localization with knockoff e-values32
Zihao Wen and David L. Dowe’s contribution to the Discussion of ‘Statistical exploration of the manifold hypothesis’ by Whiteley et al.30
Image response regression via deep neural networks30
Safaa K. Kadhem’s contribution to the Discussion on ‘Statistical exploration of the manifold hypothesis’ by Nick Whiteley, Annie Grayb, and Patrick Rubin-Delanchy30
Safe testing29
Yinqiu He, Yuqi Gu and Zhilian Ying's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng27
Isadora Antoniano Villalobos's contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes and Walker26
Proposer of the vote of thanks to Whiteley et al. and contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’25
Using a two-parameter sensitivity analysis framework to efficiently combine randomized and nonrandomized studies23
Scalability of Metropolis-within-Gibbs schemes for high-dimensional Bayesian models22
Anytime validity is free: inducing sequential tests19
Statistical testing under distributional shifts19
Computationally efficient and data-adaptive changepoint inference in high dimension18
Covariate adjustment in multiarmed, possibly factorial experiments18
Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods18
Proximal survival analysis to handle dependent right censoring18
Adaptive bootstrap tests for composite null hypotheses in the mediation pathway analysis17
SymmPI: predictive inference for data with group symmetries17
Corrected generalized cross-validation for finite ensembles of penalized estimators17
Pitman efficiency lower bounds for multivariate distribution-free tests based on optimal transport17
16
Glenn Shafer’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen16
Ying Zhou and Xinyi Zhang's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng15
Ramses Mena Chavez's contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes and Walker15
Rungang Han and Anru R. Zhangs contribution to the Discussion of ‘Vintage factor analysis with varimax performs statistical inference’ by Rohe & Zeng15
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’14
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 machi14
Strong oracle guarantees for partial penalized tests of high-dimensional generalized linear models14
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’13
Graphical criteria for the identification of marginal causal effects in continuous-time survival and event-history analyses13
Randomisation inference beyond the sharp null: bounded null hypotheses and quantiles of individual treatment effects12
Tian, Liu and Tan's contribution to the Discussion of ‘Augmented balancing weights as linear regression’ by Bruns-Smith et al12
Testing many constraints in possibly irregular models using incomplete U-statistics11
Bootstrapping estimators based on the block maxima method11
Conformal prediction with local weights: randomization enables robust guarantees11
Robust model averaging prediction of longitudinal response with ultrahigh-dimensional covariates11
Estimating the efficiency gain of covariate-adjusted analyses in future clinical trials using external data10
Hernando Ombao’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’10
Broadcasted nonparametric tensor regression10
Thomas S. Richardson’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes10
Seconder of the vote of thanks to Bruns-Smith et al. and contribution to the Discussion of ‘Augmented balancing weights as linear regression'10
Andrej Srakar’s contribution to the Discussion of ‘Root and community inference on the latent growth process of a network’ by Crane and Xu10
A unified generalization of the inverse regression methods via column selection10
Conformalized survival analysis10
Cluster extent inference revisited: quantification and localisation of brain activity10
Correction to: Semi-supervised approaches to efficient evaluation of model prediction performance9
Engression: extrapolation through the lens of distributional regression9
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 Xu9
Safaa K. Kadhem's contribution to the Discussion of ‘Augmented balancing weights as linear regression' by Bruns-Smith et al9
Anthony C Davison and Igor Rodionov’s contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas9
Spectral change point estimation for high-dimensional time series by sparse tensor decomposition9
Scalable couplings for the random walk Metropolis algorithm9
Kuldeep Kumar's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng9
Estimating heterogeneous treatment effects with right-censored data via causal survival forests9
Orthogonalized moment aberration for mixed-level multi-stratum factorial designs with partially-relaxed orthogonal block structures9
Bertrand Clarke's contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes and Walker9
Ivor Cribben and Anastasiou Andreas’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’9
Gradient synchronization for multivariate functional data, with application to brain connectivity8
Gesine Reinert’s contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al.8
Identification and estimation of causal peer effects using double negative controls for unmeasured network confounding8
Penalized empirical likelihood over decentralized networks8
Tyler J. VanderWeele's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng8
The synthetic instrument: from sparse association to sparse causation8
Root cause discovery via permutations and Cholesky decomposition8
A general framework for cutting feedback within modularized Bayesian inference8
Simon et al.’s contribution to the Discussion of ‘Statistical exploration of the Manifold Hypothesis’ by Whiteley et al.7
Goodness-of-fit tests for high-dimensional Gaussian graphical models via exchangeable sampling7
Universal Prediction Band via Semi-Definite Programming7
Oliver Hines and Karla Diaz-Ordazʼs Contribution to the Discussion of ‘Assumption-Lean Inference For Generalised Linear Model Parameters’ by Vansteelandt and Dukes7
Martin Larsson and Johannes Ruf’s contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas7
Ryan Martin’s contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas7
Least squares for cardinal paired comparisons data6
Seconder of the vote of thanks to Rohe & Zeng and contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’6
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 Xu6
Normalised latent measure factor models6
Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data Analysis6
Autoregressive optimal transport models6
Thorsten Dickhaus’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen6
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’6
Kuldeep Kumar’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes6
On the instrumental variable estimation with many weak and invalid instruments6
Zihao Wen and David L. Dowe’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen6
Erratum: Usable and precise asymptotics for generalized linear mixed model analysis and design6
Yudong Chen and Yining Chen’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’6
Correction to: Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods6
Adaptive functional principal components analysis6
α-separability and adjustable combination of amplitude and phase model for functional data5
Model-assisted sensitivity analysis for treatment effects under unmeasured confounding via regularized calibrated estimation5
Inference with Mondrian random forests5
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’5
Combining evidence across filtrations5
Ordering factorial experiments5
Multi-task learning for sparsity pattern heterogeneity: statistical and computational perspectives5
Model privacy: a unified framework for understanding model stealing attacks and defences5
Andrej Srakar’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen5
Correction to: Holdout predictive checks for Bayesian model criticism5
Correction to: Ordering factorial experiments5
Semiparametric localized principal stratification analysis with continuous strata5
Convexity and measures of statistical association5
Post-detection inference for sequential changepoint localization5
Priyantha Wijayatunga’s contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes, and Walker5
Autoregressive networks with dependent edges5
Graphical methods for Order-of-Addition experiments4
Yunxiao Chen and Gongjun Xu's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng4
Randomized empirical likelihood test for ultra-high dimensional means under general covariances4
Stratification pattern enumerator and its applications4
Trace-class Gaussian priors for Bayesian learning of neural networks with MCMC4
Multi-resolution subsampling for linear classification with massive data4
Analytic natural gradient updates for Cholesky factor in Gaussian variational approximation4
Derandomised knockoffs: leveraging e-values for false discovery rate control4
CovNet: Covariance Networks for Functional Data on Multidimensional Domains4
Jiaqi Gu and Guosheng Yin’s contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes and Walker4
Debiased inference for a covariate-adjusted regression function4
Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer’s disease4
Peng Ding’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes4
Ensemble methods for testing a global null4
Semi-parametric tensor factor analysis by iteratively projected singular value decomposition4
Shakeel Gavioli-Akilagun’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’4
Testing homogeneity: the trouble with sparse functional data4
Scalable Bayesian inference for heat kernel Gaussian processes on manifolds4
Contents of Volume 84, 20224
Estimating means of bounded random variables by betting4
Sparse Kronecker product decomposition: a general framework of signal region detection in image regression4
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