Journal of the Royal Statistical Society Series B-Statistical Methodol

Papers
(The median citation count of Journal of the Royal Statistical Society Series B-Statistical Methodol is 0. 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.)
ArticleCitations
Mark Pilling's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng113
Strategic two-sample test via the two-armed bandit process76
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-values40
Correlation adjusted debiased Lasso: debiasing the Lasso with inaccurate covariate model40
Stefano Rizzelli’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen39
Maozai Tian, Keming Yu and Jiangfeng Wang’s contribution to the Discussion of ‘Safe testing’ by Grünwald, De Heide, and Koolen38
Image response regression via deep neural networks35
On Functional Processes with Multiple Discontinuities34
Safe testing33
Yinqiu He, Yuqi Gu and Zhilian Ying's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng32
Issue Information28
Corrected generalized cross-validation for finite ensembles of penalized estimators27
SymmPI: predictive inference for data with group symmetries26
Isadora Antoniano Villalobos's contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes and Walker22
Proximal survival analysis to handle dependent right censoring22
Covariate adjustment in multiarmed, possibly factorial experiments21
Computationally efficient and data-adaptive changepoint inference in high dimension21
Statistical testing under distributional shifts21
Adaptive bootstrap tests for composite null hypotheses in the mediation pathway analysis21
Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods20
Synthetic Controls with Staggered Adoption20
Ramses Mena Chavez's contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes and Walker20
Rungang Han and Anru R. Zhangs contribution to the Discussion of ‘Vintage factor analysis with varimax performs statistical inference’ by Rohe & Zeng19
Glenn Shafer’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen18
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Robust model averaging prediction of longitudinal response with ultrahigh-dimensional covariates18
Randomisation inference beyond the sharp null: bounded null hypotheses and quantiles of individual treatment effects17
Conformal prediction with local weights: randomization enables robust guarantees17
Strong oracle guarantees for partial penalized tests of high-dimensional generalized linear models17
Ying Zhou and Xinyi Zhang's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng16
Estimating the efficiency gain of covariate-adjusted analyses in future clinical trials using external data16
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 machi15
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-statistics14
Conformalized survival analysis14
Transfer Learning for High-Dimensional Linear Regression: Prediction, Estimation and Minimax Optimality14
Graphical criteria for the identification of marginal causal effects in continuous-time survival and event-history analyses14
Bayesian Context Trees: Modelling and Exact Inference for Discrete Time Series13
Thomas S. Richardson’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes13
Andrej Srakar’s contribution to the Discussion of ‘Root and community inference on the latent growth process of a network’ by Crane and Xu12
Broadcasted nonparametric tensor regression12
Correction to: Semi-supervised approaches to efficient evaluation of model prediction performance12
Estimating heterogeneous treatment effects with right-censored data via causal survival forests12
Empirical Bayes PCA in High Dimensions12
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 activity12
Graph Based Gaussian Processes on Restricted Domains11
Engression: extrapolation through the lens of distributional regression11
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 Walker10
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 confounding10
Tyler J. VanderWeele's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng10
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
A general framework for cutting feedback within modularized Bayesian inference9
Gradient synchronization for multivariate functional data, with application to brain connectivity9
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
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
Authors’ Reply to the Discussion of ‘Gaussian Differential Privacy’ by Donget al.9
Efficient Manifold Approximation with Spherelets9
Martin Larsson and Johannes Ruf’s contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas8
Adaptive functional principal components analysis8
Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data Analysis8
Two-Sample Inference for High-Dimensional Markov Networks8
Issue Information8
Universal Prediction Band via Semi-Definite Programming8
Autoregressive optimal transport models8
Normalised latent measure factor models8
Ryan Martin’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
On the instrumental variable estimation with many weak and invalid instruments8
Zihao Wen and David L. Dowe’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen8
Convexity and measures of statistical association7
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
Erratum: Usable and precise asymptotics for generalized linear mixed model analysis and design7
Kuldeep Kumar’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes7
Correction to: Ordering factorial experiments7
Ordering factorial experiments7
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
Model-assisted sensitivity analysis for treatment effects under unmeasured confounding via regularized calibrated estimation7
Issue Information7
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
Analysis of Networks via the Sparseβ-model7
α-separability and adjustable combination of amplitude and phase model for functional data7
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
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
Multi-resolution subsampling for linear classification with massive data6
Correction to: Holdout predictive checks for Bayesian model criticism6
Priyantha Wijayatunga’s contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes, and Walker6
Andrej Srakar’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen6
Graphical methods for Order-of-Addition experiments5
Analytic natural gradient updates for Cholesky factor in Gaussian variational approximation5
Estimating means of bounded random variables by betting5
Peng Ding’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes5
CovNet: Covariance Networks for Functional Data on Multidimensional Domains5
Jiaqi Gu and Guosheng Yin’s contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes and Walker5
Ensemble methods for testing a global null5
Derandomised knockoffs: leveraging e-values for false discovery rate control5
Semi-parametric tensor factor analysis by iteratively projected singular value decomposition5
Sparse Kronecker product decomposition: a general framework of signal region detection in image regression5
Trace-class Gaussian priors for Bayesian learning of neural networks with MCMC4
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
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 Covariates4
Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer’s disease4
Stratification pattern enumerator and its applications4
Steven R Howard's contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas4
Optimal Statistical Inference for Individualized Treatment Effects in High-Dimensional Models4
Randomized empirical likelihood test for ultra-high dimensional means under general covariances4
Testing homogeneity: the trouble with sparse functional data4
Debiased inference for a covariate-adjusted regression function4
Yang Liu's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng4
A fast asynchronous Markov chain Monte Carlo sampler for sparse Bayesian inference4
Contents of Volume 84, 20224
Yunxiao Chen and Gongjun Xu's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng4
Martin Larsson, Aaditya Ramdas, and Johannes Ruf’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen3
Dimension-Free Mixing for High-Dimensional Bayesian Variable Selection3
Non-parametric inference about mean functionals of non-ignorable non-response data without identifying the joint distribution3
Selecting informative conformal prediction sets with false coverage rate control3
Semiparametric Estimation for Causal Mediation Analysis with Multiple Causally Ordered Mediators3
Isotonic subgroup selection3
The Sceptical Bayes Factor for the Assessment of Replication Success3
Testing high-dimensional multinomials with applications to text analysis3
Causal inference with invalid instruments: post-selection problems and a solution using searching and sampling3
Minimax detection boundary and sharp optimal test for Gaussian graphical models3
Prediction and Outlier Detection in Classification Problems3
Tianxi Li’s contribution to the Discussion of ‘Root and community inference on the latent growth process of a network’ by Crane and Xu3
Policy evaluation for temporal and/or spatial dependent experiments3
Martingale posterior distributions3
Kolyan Ray and Botond Szabo's contribution to the Discussion of ‘Martingale Posterior Distributions’ by Fong, Holmes and Walker3
Two-phase rejective sampling and its asymptotic properties3
Model-Assisted Analyses of Cluster-Randomized Experiments3
Philip B. Stark’s contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas3
Robustness, model checking, and hierarchical models2
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Probabilistic Richardson extrapolation2
Richard Guo’s contribution to the Discussion of ‘Parameterizing and simulating from causal models’ by Evans and Didelez2
Nonparametric, Tuning-Free Estimation of S-Shaped Functions2
Bo Zhang’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’2
David Huk, Lorenzo Pacchiardi, Ritabrata Dutta and Mark Steel's contribution to the Discussion of ‘Martingale posterior distributions’ by Fong, Holmes and Walker2
Rank-transformed subsampling: inference for multiple data splitting and exchangeable p-values2
Supervised Multivariate Learning with Simultaneous Feature Auto-Grouping and Dimension Reduction2
Long-term causal inference under persistent confounding via data combination2
Ayla Jungbluth and Johannes Lederer’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’2
Joshua Cape's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng2
Proposer of the vote of thanks to Rohe & Zeng and contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’2
Empirical Likelihood-Based Inference for Functional Means with Application to Wearable Device Data2
David Draper and Erdong Guo's contribution to the discussion of ‘Martingale posterior distributions’, by Fong, Holmes and Walker2
Erratum: Anchor Regression: Heterogeneous Data Meet Causality2
Joris Mulder’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen2
Robust estimation and inference for expected shortfall regression with many regressors2
Errata to “Functional Models for Time-Varying Random Objects”2
Junhui Cai, Dan Yang, Linda Zhao and Wu Zhu's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng2
Inferential Wasserstein Generative Adversarial Networks2
Monotone response surface of multi-factor condition: estimation and Bayes classifiers2
False Discovery Rate Control with E-values1
Optimal individualized treatment rule for combination treatments under budget constraints1
Gilbert MacKenzie’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’1
Torben Martinussen’s contribution to the Discussion of ‘Parameterizing and simulating from causal models’ by Evans and Didelez1
Multivariate, heteroscedastic empirical Bayes via nonparametric maximum likelihood1
A nonparametric framework for treatment effect modifier discovery in high dimensions1
Christine P Chai's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng1
The Confidence Interval Method for Selecting Valid Instrumental Variables1
Parameterizing and simulating from causal models1
A focusing framework for testing bi-directional causal effects in Mendelian randomization1
Graphical Criteria for Efficient Total Effect Estimation Via Adjustment in Causal Linear Models1
Xiaoyue Niu's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng1
Causal inference on distribution functions1
Another look at bandwidth-free inference: a sample splitting approach1
GRASP: a goodness-of-fit test for classification learning1
Testing for a Change in Mean after Changepoint Detection1
Alexander Ly’s contribution to the Discussion of ‘Safe testing’ by Grünwald, de Heide, and Koolen1
Doubly robust calibration of prediction sets under covariate shift1
Frederic Schoenberg and Weng Kee Wong’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’1
Issue Information1
Christine P. Chai's Contribution to the Discussion of ‘Gaussian Differential Privacy’ by Donget al.1
High-Dimensional Quantile Regression: Convolution Smoothing and Concave Regularization1
Bayesian Inference for Risk Minimization via Exponentially Tilted Empirical Likelihood1
Bayesian inference with thel1-ball prior: solving combinatorial problems with exact zeros1
Estimating a directed tree for extremes1
Permutation-based true discovery guarantee by sum tests1
Isotonic Distributional Regression1
Modelling matrix time series via a tensor CP-decomposition1
A Kernel-Expanded Stochastic Neural Network1
On the Cross-Validation Bias due to Unsupervised Preprocessing1
General Bayesian Loss Function Selection and the use of Improper Models1
From denoising diffusions to denoising Markov models1
Confidence on the focal: conformal prediction with selection-conditional coverage1
Bayesian Estimation and Comparison of Conditional Moment Models1
Correction to: X-vine models for multivariate extremes1
Wenkai Xu’s contribution to the Discussion of ‘Safe testing’ by Grünwald, De Heide and Koolen1
Leveraging the Fisher Randomization Test using Confidence Distributions: Inference, Combination and Fusion Learning1
Seconder of the vote of thanks to Grünwald, de Heide, and Koolen and contribution to the Discussion of ‘Safe testing’1
Dynamic synthetic control method for evaluating treatment effects in auto-regressive processes1
Authors' reply to the Discussion of ‘Martingale Posterior Distributions’1
Priyantha Wijayatunga’s Contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al.1
Covariate Powered Cross-Weighted Multiple Testing1
Augmented balancing weights as linear regression1
Konstantin Siroki and Korbinian Strimmer’s contribution to the Discussion of ‘Vintage factor analysis with varimax performs statistical inference’ by Rohe and Zeng1
Art Owen’s contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas1
Informative core identification in complex networks1
Proposer of the Vote of Thanks to Dong et al. and Contribution to the Discussion of ‘Gaussian Differential Privacy’0
Vintage factor analysis with Varimax performs statistical inference0
Proposer of the Vote of Thanks and Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes0
Gaussian Prepivoting for Finite Population Causal Inference0
Designing to detect heteroscedasticity in a regression model0
Ilya Shpitser’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes0
Controlling the false discovery rate in transformational sparsity: Split Knockoffs0
Modelling the COVID-19 Infection Trajectory: A Piecewise Linear Quantile Trend Model0
Sebastian Dietz’s Contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al.0
Seconder of the vote of thanks and contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’0
Functional Peaks-Over-Threshold Analysis0
A Graph-Theoretic Approach to Randomization Tests of Causal Effects under General Interference0
Covariate-adaptive randomization inference in matched designs0
Non-Reversible Parallel Tempering: A Scalable Highly Parallel MCMC Scheme0
Statistical inference for multivariate extremes via a geometric approach0
0
Rong Jiang and Keming Yu's contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas0
Two-way dynamic factor models for high-dimensional matrix-valued time series0
Correction to: Optimal and Maximin Procedures for Multiple Testing Problems0
Kaizheng Wang's contribution to the Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng0
An optimal design framework for lasso sign recovery0
Core shrinkage covariance estimation for matrix-variate data0
Conformal Inference of Counterfactuals and Individual Treatment Effects0
Model Identification Via Total Frobenius Norm of Multivariate Spectra0
Robust Generalised Bayesian Inference for Intractable Likelihoods0
David R. Bickel’s contribution to the Discussion of ‘Safe testing’ by Grünwald, De Heide, and Koolen0
The variational method of moments0
Statistical inference for high-dimensional panel functional time series0
Linear Regression and Its Inference on Noisy Network-Linked Data0
Jiwei Zhao’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes0
A stableness of resistance model for nonresponse adjustment with callback data0
Bayesian predictive decision synthesis0
Eric J Tchetgen Tchetgen’s Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes0
Jorge Mateu's contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’0
Gregor Steiner and Mark Steel’s contribution to the Discussion of ‘Parameterizing and simulating from causal models’ by Evans and Didelez0
Philip S Thomas, Erik Learned-Miller and My Phan's contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas0
Seconder of the vote of thanks to Fong, Holmes and Walker and contribution to the Discussion of ‘Martingale Posterior Distributions’0
Selective Inference for Effect Modification Via the Lasso0
Corrigendum: Exact Bayesian inference in spatiotemporal Cox processes driven by multivariate Gaussian processes0
Quasi-Newton updating for large-scale distributed learning0
Prediction sets adaptive to unknown covariate shift0
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