Journal of the Royal Statistical Society Series B

1997 - 2022

Current editor(s): P. Fryzlewicz and I. Van Keilegom

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Volume 84, issue 5, 2022

Modelling the COVID‐19 infection trajectory: A piecewise linear quantile trend model pp. 1589-1607

Feiyu Jiang, Zifeng Zhao and Xiaofeng Shao

Calibrating the scan statistic: Finite sample performance versus asymptotics pp. 1608-1639

Guenther Walther and Andrew Perry

General Bayesian loss function selection and the use of improper models pp. 1640-1665

Jack Jewson and David Rossell

Exact clustering in tensor block model: Statistical optimality and computational limit pp. 1666-1698

Rungang Han, Yuetian Luo, Miaoyan Wang and Anru R. Zhang

Segmenting time series via self‐normalisation pp. 1699-1725

Zifeng Zhao, Feiyu Jiang and Xiaofeng Shao

An approximation algorithm for blocking of an experimental design pp. 1726-1750

Bikram Karmakar

Dimension‐free mixing for high‐dimensional Bayesian variable selection pp. 1751-1784

Quan Zhou, Jun Yang, Dootika Vats, Gareth O. Roberts and Jeffrey S. Rosenthal

CovNet: Covariance networks for functional data on multidimensional domains pp. 1785-1820

Soham Sarkar and Victor M. Panaretos

Conditional independence testing in Hilbert spaces with applications to functional data analysis pp. 1821-1850

Anton Rask Lundborg, Rajen D. Shah and Jonas Peters

Linear regression and its inference on noisy network‐linked data pp. 1851-1885

Can M. Le and Tianxi Li

ZAP: Z$$ Z $$‐value adaptive procedures for false discovery rate control with side information pp. 1886-1946

Dennis Leung and Wenguang Sun

Empirical likelihood‐based inference for functional means with application to wearable device data pp. 1947-1968

Hsin‐wen Chang and Ian W. McKeague

Causal inference with spatio‐temporal data: Estimating the effects of airstrikes on insurgent violence in Iraq pp. 1969-1999

Georgia Papadogeorgou, Kosuke Imai, Jason Lyall and Fan Li

High‐dimensional principal component analysis with heterogeneous missingness pp. 2000-2031

Ziwei Zhu, Tengyao Wang and Richard J. Samworth

A statistical test to reject the structural interpretation of a latent factor model pp. 2032-2054

Tyler J. VanderWeele and Stijn Vansteelandt

Structure learning for extremal tree models pp. 2055-2087

Sebastian Engelke and Stanislav Volgushev

Volume 84, issue 4, 2022

Optimal thinning of MCMC output pp. 1059-1081

Marina Riabiz, Wilson Ye Chen, Jon Cockayne, Pawel Swietach, Steven A. Niederer, Lester Mackey and Chris. J. Oates

Testing for a change in mean after changepoint detection pp. 1082-1104

Sean Jewell, Paul Fearnhead and Daniela Witten

Optimal and maximin procedures for multiple testing problems pp. 1105-1128

Saharon Rosset, Ruth Heller, Amichai Painsky and Ehud Aharoni

Efficient manifold approximation with spherelets pp. 1129-1149

Didong Li, Minerva Mukhopadhyay and David B. Dunson

Bootstrap inference for the finite population mean under complex sampling designs pp. 1150-1174

Zhonglei Wang, Liuhua Peng and Jae Kwang Kim

Semiparametric latent class analysis of recurrent event data pp. 1175-1197

Wei Zhao, Limin Peng and John Hanfelt

Fast increased fidelity samplers for approximate Bayesian Gaussian process regression pp. 1198-1228

Kelly R. Moran and Matthew W. Wheeler

Manifold Markov chain Monte Carlo methods for Bayesian inference in diffusion models pp. 1229-1256

Matthew M. Graham, Alexandre H. Thiery and Alexandros Beskos

Bayesian inference for risk minimization via exponentially tilted empirical likelihood pp. 1257-1286

Rong Tang and Yun Yang

Bayesian context trees: Modelling and exact inference for discrete time series pp. 1287-1323

Ioannis Kontoyiannis, Lambros Mertzanis, Athina Panotopoulou, Ioannis Papageorgiou and Maria Skoularidou

Nonparametric, tuning‐free estimation of S‐shaped functions pp. 1324-1352

Oliver Y. Feng, Yining Chen, Qiyang Han, Raymond J. Carroll and Richard J. Samworth

Efficient evaluation of prediction rules in semi‐supervised settings under stratified sampling pp. 1353-1391

Jessica Gronsbell, Molei Liu, Lu Tian and Tianxi Cai

Functional peaks‐over‐threshold analysis pp. 1392-1422

Raphaël de Fondeville and Anthony C. Davison

Multiply robust estimation of causal effects under principal ignorability pp. 1423-1445

Zhichao Jiang, Shu Yang and Peng Ding

A statistical interpretation of spectral embedding: The generalised random dot product graph pp. 1446-1473

Patrick Rubin‐Delanchy, Joshua Cape, Minh Tang and Carey E. Priebe

On the cross‐validation bias due to unsupervised preprocessing pp. 1474-1502

Amit Moscovich and Saharon Rosset

Paired or partially paired two‐sample tests with unordered samples pp. 1503-1525

Yudong Wang, Yanlin Tang and Zhi‐Sheng Ye

The Debiased Spatial Whittle likelihood pp. 1526-1557

Arthur P. Guillaumin, Adam M. Sykulski, Sofia C. Olhede and Frederik J. Simons

Universal prediction band via semi‐definite programming pp. 1558-1580

Tengyuan Liang

Corrigendum to ‘Simulation of multivariate diffusion bridges’ pp. 1581-1585

Mogens Bladt, Samuel Finch and Michael Sørensen

Volume 84, issue 3, 2022

Assumption‐lean inference for generalised linear model parameters pp. 657-685

Stijn Vansteelandt and Oliver Dukes

Proposer of the vote of thanks and contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 686-689

Rhian M. Daniel

Seconder of the vote of thanks to Vansteelandt and Dukes and contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ pp. 689-691

Vanessa Didelez

Peng Ding’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 691-693

Peng Ding

Mats J Stensrud and Aaron L. Sarvet’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 694-696

Mats J. Stensrud and Aaron L. Sarvet

Heather Battey’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 696-698

Heather Battey

Christian Hennig's contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 698-699

Christian Hennig

Pallavi Basuʼs contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 700-701

Pallavi Basu

Blair Bilodeau's contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 701-702

Blair Bilodeau

Andreas Buja, Richard A. Berk, Arun K. Kuchibhotla, Linda Zhao and Ed George’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 703-705

Andreas Buja, Richard A. Berk, Arun K. Kuchibhotla, Linda Zhao and Ed George

Anna Choi and Weng Kee Wong’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 705-706

Anna Choi and Weng Kee Wong

Chaohua Dong, Jiti Gao and Oliver Linton’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 707-708

Chaohua Dong, Jiti Gao and Oliver Linton

Oliver Hines and Karla Diaz‐Ordazʼs contribution to the discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 709-710

Oliver Hines and Karla Diaz‐Ordaz

Ian Hunt's contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 711-712

Ian Hunt

Kuldeep Kumar’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 712-713

Kuldeep Kumar

Michael Lavine and James Hodges’ contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 713-714

Michael Lavine and James Hodges

Elizabeth L Ogburn, Junhui Cai, Arun K Kuchibhotla, Richard A Berk and Andreas Buja’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 715-716

Elizabeth L. Ogburn, Junhui Cai, Arun K. Kuchibhotla, Richard A. Berk and Andreas Buja

Rachael V. Phillips and Mark J. van der Laan’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 717-718

Rachael V. Phillips and Mark J. van der Laan

Thomas S. Richardson’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 719-720

Thomas S. Richardson

Ilya Shpitser’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 720-721

Ilya Shpitser

Yanbo Tang's contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 722-723

Yanbo Tang

Eric J Tchetgen Tchetgen’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 723-725

Eric J. Tchetgen Tchetgen

Jiwei Zhao’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 725-726

Jiwei Zhao

Niwen Zhou and Xu Guo’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 727-729

Niwen Zhou and Xu Guo

Authors' reply to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes pp. 729-739

Stijn Vansteelandt and Oliver Dukes

Bayesian estimation and comparison of conditional moment models pp. 740-764

Siddhartha Chib, Minchul Shin and Anna Simoni

Statistical inference of the value function for reinforcement learning in infinite‐horizon settings pp. 765-793

Chengchun Shi, Sheng Zhang, Wenbin Lu and Rui Song

Semiparametric estimation for causal mediation analysis with multiple causally ordered mediators pp. 794-821

Xiang Zhou

False discovery rate control with e‐values pp. 822-852

Ruodu Wang and Aaditya Ramdas

Empirical Bayes PCA in high dimensions pp. 853-878

Xinyi Zhong, Chang Su and Zhou Fan

The sceptical Bayes factor for the assessment of replication success pp. 879-911

Samuel Pawel and Leonhard Held

Supervised multivariate learning with simultaneous feature auto‐grouping and dimension reduction pp. 912-932

Yiyuan She, Jiahui Shen and Chao Zhang

On functional processes with multiple discontinuities pp. 933-972

Jialiang Li, Yaguang Li and Tailen Hsing

Coupling‐based convergence assessment of some Gibbs samplers for high‐dimensional Bayesian regression with shrinkage priors pp. 973-996

Niloy Biswas, Anirban Bhattacharya, Pierre E. Jacob and James E. Johndrow

Robust generalised Bayesian inference for intractable likelihoods pp. 997-1022

Takuo Matsubara, Jeremias Knoblauch, François‐Xavier Briol and Chris J. Oates

High‐dimensional changepoint estimation with heterogeneous missingness pp. 1023-1055

Bertille Follain, Tengyao Wang and Richard J. Samworth

Volume 84, issue 2, 2022

On efficient dimension reduction with respect to the interaction between two response variables pp. 269-294

Wei Luo

Gaussian prepivoting for finite population causal inference pp. 295-320

Peter L. Cohen and Colin B. Fogarty

Non‐reversible parallel tempering: A scalable highly parallel MCMC scheme pp. 321-350

Saifuddin Syed, Alexandre Bouchard‐Côté, George Deligiannidis and Arnaud Doucet

Synthetic controls with staggered adoption pp. 351-381

Eli Ben‐Michael, Avi Feller and Jesse Rothstein

Selective inference for effect modification via the lasso pp. 382-413

Qingyuan Zhao, Dylan S. Small and Ashkan Ertefaie

Graph based Gaussian processes on restricted domains pp. 414-439

David B. Dunson, Hau‐Tieng Wu and Nan Wu

Efficient learning of optimal individualized treatment rules for heteroscedastic or misspecified treatment‐free effect models pp. 440-472

Weibin Mo and Yufeng Liu

Model identification via total Frobenius norm of multivariate spectra pp. 473-495

Tucker McElroy and Anindya Roy

The Barker proposal: Combining robustness and efficiency in gradient‐based MCMC pp. 496-523

Samuel Livingstone and Giacomo Zanella

Prediction and outlier detection in classification problems pp. 524-546

Leying Guan and Robert Tibshirani

A kernel‐expanded stochastic neural network pp. 547-578

Yan Sun and Faming Liang

Graphical criteria for efficient total effect estimation via adjustment in causal linear models pp. 579-599

Leonard Henckel, Emilija Perković and Marloes H. Maathuis

Functional structural equation model pp. 600-629

Kuang‐Yao Lee and Lexin Li

SIMPLE: Statistical inference on membership profiles in large networks pp. 630-653

Jianqing Fan, Yingying Fan, Xiao Han and Jinchi Lv

Volume 84, issue 1, 2022

Gaussian differential privacy pp. 3-37

Jinshuo Dong, Aaron Roth and Weijie J. Su

Proposer of the vote of thanks to Dong et al. and contribution to the Discussion of ‘Gaussian Differential Privacy’ pp. 37-38

Borja Balle

Seconder of the vote of thanks to Dong et al. and contribution to the Discussion of ‘Gaussian Differential Privacy’ pp. 39-41

Marco Avella‐Medina

Peter Krusche and Frank Bretz's contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al pp. 42-43

Peter Krusche and Frank Bretz

Christine P. Chai's contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al pp. 43-44

Christine P. Chai

Sebastian Dietz’s contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al pp. 44-45

Sebastian Dietz

J. Goseling and M.N.M. van Lieshout's contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al pp. 46-47

J. Goseling and M.N.M. van Lieshout

Jorge Mateu’s contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al pp. 47-48

Jorge Mateu

Priyantha Wijayatunga’s contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al pp. 49-50

Priyantha Wijayatunga

Authors’ reply to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al pp. 50-54

Jinshuo Dong, Aaron Roth and Weijie J. Su

Usable and precise asymptotics for generalized linear mixed model analysis and design pp. 55-82

Jiming Jiang, Matt P. Wand and Aishwarya Bhaskaran

Inferential Wasserstein generative adversarial networks pp. 83-113

Yao Chen, Qingyi Gao and Xiao Wang

Waste‐free sequential Monte Carlo pp. 114-148

Hai‐Dang Dau and Nicolas Chopin

Transfer learning for high‐dimensional linear regression: Prediction, estimation and minimax optimality pp. 149-173

Sai Li, T. Tony Cai and Hongzhe Li

A graph‐theoretic approach to randomization tests of causal effects under general interference pp. 174-204

David Puelz, Guillaume Basse, Avi Feller and Panos Toulis

High‐dimensional quantile regression: Convolution smoothing and concave regularization pp. 205-233

Kean Ming Tan, Lan Wang and Wen‐Xin Zhou

High‐dimensional, multiscale online changepoint detection pp. 234-266

Yudong Chen, Tengyao Wang and Richard J. Samworth