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 2021-08-01 to 2025-08-01.)
ArticleCitations
Mark Pilling's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng131
Strategic two-sample test via the two-armed bandit process88
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’63
Seconder of the vote of thanks to Evans and Didelez and contribution to the Discussion of ‘Parameterizing and simulating from causal models’57
Catch me if you can: signal localization with knockoff e-values47
Stefano Rizzelli’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen45
Correlation adjusted debiased Lasso: debiasing the Lasso with inaccurate covariate model40
On Functional Processes with Multiple Discontinuities39
Image response regression via deep neural networks39
Maozai Tian, Keming Yu and Jiangfeng Wang’s contribution to the Discussion of ‘Safe testing’ by Grünwald, De Heide, and Koolen38
Safe testing36
Yinqiu He, Yuqi Gu and Zhilian Ying's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng35
Issue Information32
Corrected generalized cross-validation for finite ensembles of penalized estimators30
Covariate adjustment in multiarmed, possibly factorial experiments29
Isadora Antoniano Villalobos's contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes and Walker27
Statistical testing under distributional shifts26
Proximal survival analysis to handle dependent right censoring26
Synthetic Controls with Staggered Adoption23
Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods23
Computationally efficient and data-adaptive changepoint inference in high dimension23
Adaptive bootstrap tests for composite null hypotheses in the mediation pathway analysis22
SymmPI: predictive inference for data with group symmetries22
Rungang Han and Anru R. Zhangs contribution to the Discussion of ‘Vintage factor analysis with varimax performs statistical inference’ by Rohe & Zeng22
Ramses Mena Chavez's contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes and Walker22
21
Glenn Shafer’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen21
Robust model averaging prediction of longitudinal response with ultrahigh-dimensional covariates20
Ying Zhou and Xinyi Zhang's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng19
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’19
Estimating the efficiency gain of covariate-adjusted analyses in future clinical trials using external data19
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 machi18
A unified generalization of the inverse regression methods via column selection17
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’17
Testing many constraints in possibly irregular models using incomplete U-statistics16
Randomisation inference beyond the sharp null: bounded null hypotheses and quantiles of individual treatment effects16
Strong oracle guarantees for partial penalized tests of high-dimensional generalized linear models15
Conformal prediction with local weights: randomization enables robust guarantees15
Thomas S. Richardson’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes15
Conformalized survival analysis15
Transfer Learning for High-Dimensional Linear Regression: Prediction, Estimation and Minimax Optimality15
Graphical criteria for the identification of marginal causal effects in continuous-time survival and event-history analyses15
Hernando Ombao’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’14
Cluster extent inference revisited: quantification and localisation of brain activity13
Correction to: Semi-supervised approaches to efficient evaluation of model prediction performance13
Empirical Bayes PCA in High Dimensions13
Andrej Srakar’s contribution to the Discussion of ‘Root and community inference on the latent growth process of a network’ by Crane and Xu13
Broadcasted nonparametric tensor regression12
Engression: extrapolation through the lens of distributional regression12
Graph Based Gaussian Processes on Restricted Domains12
Bayesian Context Trees: Modelling and Exact Inference for Discrete Time Series12
Orthogonalized moment aberration for mixed-level multi-stratum factorial designs with partially-relaxed orthogonal block structures11
Tyler J. VanderWeele's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng11
Identification and estimation of causal peer effects using double negative controls for unmeasured network confounding11
Ivor Cribben and Anastasiou Andreas’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’11
Estimating heterogeneous treatment effects with right-censored data via causal survival forests11
Bertrand Clarke's contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes and Walker11
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 Xu11
J. Goseling and M.N.M. van Lieshout's Contribution to the Discussion of ‘Gaussian Differential Privacy’ by Donget al.11
Seconder of the Vote of Thanks to Donget al.and Contribution to the Discussion of ‘Gaussian Differential Privacy’11
Anthony C Davison and Igor Rodionov’s contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas11
Authors’ Reply to the Discussion of ‘Gaussian Differential Privacy’ by Donget al.10
A general framework for cutting feedback within modularized Bayesian inference10
Gradient synchronization for multivariate functional data, with application to brain connectivity10
Efficient Manifold Approximation with Spherelets10
Kuldeep Kumar's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng10
Scalable couplings for the random walk Metropolis algorithm10
Martin Larsson and Johannes Ruf’s contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas9
Universal Prediction Band via Semi-Definite Programming9
Two-Sample Inference for High-Dimensional Markov Networks9
Issue Information9
Autoregressive optimal transport models9
Oliver Hines and Karla Diaz-Ordazʼs Contribution to the Discussion of ‘Assumption-Lean Inference For Generalised Linear Model Parameters’ by Vansteelandt and Dukes9
Ryan Martin’s contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas9
Zihao Wen and David L. Dowe’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen9
Adaptive functional principal components analysis8
Goodness-of-fit tests for high-dimensional Gaussian graphical models via exchangeable sampling8
Least squares for cardinal paired comparisons data8
Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data Analysis8
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 Koolen7
Semiparametric localized principal stratification analysis with continuous strata7
Kuldeep Kumar’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes7
Erratum: Usable and precise asymptotics for generalized linear mixed model analysis and design7
Normalised latent measure factor models7
Analysis of Networks via the Sparseβ-model7
Model-assisted sensitivity analysis for treatment effects under unmeasured confounding via regularized calibrated estimation7
On the instrumental variable estimation with many weak and invalid instruments7
Issue Information7
Peter Krusche and Frank Bretz's Contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al.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 Xu7
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
α-separability and adjustable combination of amplitude and phase model for functional data6
Andrej Srakar’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen6
Ordering factorial experiments6
Priyantha Wijayatunga’s contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes, and Walker6
Convexity and measures of statistical association6
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’6
Correction to: Ordering factorial experiments6
Estimating means of bounded random variables by betting5
Ensemble methods for testing a global null5
Multi-resolution subsampling for linear classification with massive data5
Shakeel Gavioli-Akilagun’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’5
Semi-parametric tensor factor analysis by iteratively projected singular value decomposition5
CovNet: Covariance Networks for Functional Data on Multidimensional Domains5
Derandomised knockoffs: leveraging e-values for false discovery rate control5
Graphical methods for Order-of-Addition experiments5
Peng Ding’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes5
Sparse Kronecker product decomposition: a general framework of signal region detection in image regression5
Correction to: Holdout predictive checks for Bayesian model criticism5
Analytic natural gradient updates for Cholesky factor in Gaussian variational approximation5
Jiaqi Gu and Guosheng Yin’s contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes and Walker5
A fast asynchronous Markov chain Monte Carlo sampler for sparse Bayesian inference4
Debiased inference for a covariate-adjusted regression function4
Stratification pattern enumerator and its applications4
Contents of Volume 84, 20224
Optimal Statistical Inference for Individualized Treatment Effects in High-Dimensional Models4
Niwen Zhou and Xu Guo’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes4
Spherical random projection4
Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer’s disease4
Trace-class Gaussian priors for Bayesian learning of neural networks with MCMC4
Yang Liu's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng4
Steven R Howard's contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas4
Martin Larsson, Aaditya Ramdas, and Johannes Ruf’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen4
Inference of Heterogeneous Treatment Effects using Observational Data with High-Dimensional Covariates4
Testing homogeneity: the trouble with sparse functional data4
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
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