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 2022-01-01 to 2026-01-01.)
ArticleCitations
Mark Pilling's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng170
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’65
Seconder of the vote of thanks to Evans and Didelez and contribution to the Discussion of ‘Parameterizing and simulating from causal models’58
Stefano Rizzelli’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen51
Strategic two-sample test via the two-armed bandit process48
Maozai Tian, Keming Yu and Jiangfeng Wang’s contribution to the Discussion of ‘Safe testing’ by Grünwald, De Heide, and Koolen46
On Functional Processes with Multiple Discontinuities36
Correlation adjusted debiased Lasso: debiasing the Lasso with inaccurate covariate model36
Catch me if you can: signal localization with knockoff e-values32
Safe testing32
Image response regression via deep neural networks31
Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods28
Yinqiu He, Yuqi Gu and Zhilian Ying's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng28
Computationally efficient and data-adaptive changepoint inference in high dimension27
Isadora Antoniano Villalobos's contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes and Walker27
Proximal survival analysis to handle dependent right censoring27
Corrected generalized cross-validation for finite ensembles of penalized estimators25
Covariate adjustment in multiarmed, possibly factorial experiments24
SymmPI: predictive inference for data with group symmetries24
Statistical testing under distributional shifts23
Adaptive bootstrap tests for composite null hypotheses in the mediation pathway analysis22
Using a two-parameter sensitivity analysis framework to efficiently combine randomized and nonrandomized studies21
Glenn Shafer’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen19
19
Ying Zhou and Xinyi Zhang's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng18
Ramses Mena Chavez's contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes and Walker18
Rungang Han and Anru R. Zhangs contribution to the Discussion of ‘Vintage factor analysis with varimax performs statistical inference’ by Rohe & Zeng18
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 machi17
Strong oracle guarantees for partial penalized tests of high-dimensional generalized linear models17
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’17
Bootstrapping estimators based on the block maxima method17
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’16
A unified generalization of the inverse regression methods via column selection16
Robust model averaging prediction of longitudinal response with ultrahigh-dimensional covariates15
Randomisation inference beyond the sharp null: bounded null hypotheses and quantiles of individual treatment effects15
Graphical criteria for the identification of marginal causal effects in continuous-time survival and event-history analyses14
Estimating the efficiency gain of covariate-adjusted analyses in future clinical trials using external data14
Testing many constraints in possibly irregular models using incomplete U-statistics14
Conformalized survival analysis13
Correction to: Semi-supervised approaches to efficient evaluation of model prediction performance13
Thomas S. Richardson’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes13
Hernando Ombao’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’13
Conformal prediction with local weights: randomization enables robust guarantees13
Spectral change point estimation for high-dimensional time series by sparse tensor decomposition12
Broadcasted nonparametric tensor regression12
Bayesian Context Trees: Modelling and Exact Inference for Discrete Time Series12
Andrej Srakar’s contribution to the Discussion of ‘Root and community inference on the latent growth process of a network’ by Crane and Xu12
Cluster extent inference revisited: quantification and localisation of brain activity11
Engression: extrapolation through the lens of distributional regression11
Orthogonalized moment aberration for mixed-level multi-stratum factorial designs with partially-relaxed orthogonal block structures11
Empirical Bayes PCA in High Dimensions11
Estimating heterogeneous treatment effects with right-censored data via causal survival forests10
Seconder of the Vote of Thanks to Donget al.and Contribution to the Discussion of ‘Gaussian Differential Privacy’10
J. Goseling and M.N.M. van Lieshout's Contribution to the Discussion of ‘Gaussian Differential Privacy’ by Donget al.9
Kuldeep Kumar's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng9
Ivor Cribben and Anastasiou Andreas’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’9
Bertrand Clarke's contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes and Walker9
Authors’ Reply to the Discussion of ‘Gaussian Differential Privacy’ by Donget al.9
Scalable couplings for the random walk Metropolis algorithm9
Tyler J. VanderWeele's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng9
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
Martin Larsson and Johannes Ruf’s contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas8
Oliver Hines and Karla Diaz-Ordazʼs Contribution to the Discussion of ‘Assumption-Lean Inference For Generalised Linear Model Parameters’ by Vansteelandt and Dukes8
Efficient Manifold Approximation with Spherelets8
Identification and estimation of causal peer effects using double negative controls for unmeasured network confounding8
Zihao Wen and David L. Dowe’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen8
Adaptive functional principal components analysis8
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 Xu8
Gradient synchronization for multivariate functional data, with application to brain connectivity8
Ryan Martin’s contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas8
Universal Prediction Band via Semi-Definite Programming8
On the instrumental variable estimation with many weak and invalid instruments8
A general framework for cutting feedback within modularized Bayesian inference8
Root cause discovery via permutations and Cholesky decomposition8
Least squares for cardinal paired comparisons data7
Kuldeep Kumar’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes7
Seconder of the vote of thanks to Rohe & Zeng and contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’7
Autoregressive optimal transport models7
Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data Analysis7
Issue Information7
Goodness-of-fit tests for high-dimensional Gaussian graphical models via exchangeable sampling7
Erratum: Usable and precise asymptotics for generalized linear mixed model analysis and design7
Thorsten Dickhaus’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen7
α-separability and adjustable combination of amplitude and phase model for functional data6
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
Model-assisted sensitivity analysis for treatment effects under unmeasured confounding via regularized calibrated estimation6
Yudong Chen and Yining Chen’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’6
Normalised latent measure factor models6
Correction to: Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods6
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
Convexity and measures of statistical association6
Multi-task learning for sparsity pattern heterogeneity: statistical and computational perspectives5
Priyantha Wijayatunga’s contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes, and Walker5
Multi-resolution subsampling for linear classification with massive data5
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
Ordering factorial experiments5
Inference with Mondrian random forests5
Correction to: Ordering factorial experiments5
Sparse Kronecker product decomposition: a general framework of signal region detection in image regression5
Semiparametric localized principal stratification analysis with continuous strata5
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
Analytic natural gradient updates for Cholesky factor in Gaussian variational approximation5
Shakeel Gavioli-Akilagun’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’4
Estimating means of bounded random variables by betting4
Ensemble methods for testing a global null4
Semi-parametric tensor factor analysis by iteratively projected singular value decomposition4
Debiased inference for a covariate-adjusted regression function4
Randomized empirical likelihood test for ultra-high dimensional means under general covariances4
Steven R Howard's contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas4
Trace-class Gaussian priors for Bayesian learning of neural networks with MCMC4
CovNet: Covariance Networks for Functional Data on Multidimensional Domains4
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
Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer’s disease4
Stratification pattern enumerator and its applications4
A fast asynchronous Markov chain Monte Carlo sampler for sparse Bayesian inference4
Derandomised knockoffs: leveraging e-values for false discovery rate control4
Peng Ding’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes4
Jiaqi Gu and Guosheng Yin’s contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes and Walker4
Niwen Zhou and Xu Guo’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes4
Testing homogeneity: the trouble with sparse functional data4
Yang Liu's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng4
Contents of Volume 84, 20224
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