Journal of the Royal Statistical Society Series B

1997 - 2022

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

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Volume 83, issue 5, 2021

Analysis of networks via the sparse β‐model pp. 887-910

Mingli Chen, Kengo Kato and Chenlei Leng

Conformal inference of counterfactuals and individual treatment effects pp. 911-938

Lihua Lei and Emmanuel J. Candès

Two‐sample inference for high‐dimensional Markov networks pp. 939-962

Byol Kim, Song Liu and Mladen Kolar

Isotonic distributional regression pp. 963-993

Alexander Henzi, Johanna F. Ziegel and Tilmann Gneiting

Model‐assisted analyses of cluster‐randomized experiments pp. 994-1015

Fangzhou Su and Peng Ding

Inference of heterogeneous treatment effects using observational data with high‐dimensional covariates pp. 1016-1043

Yumou Qiu, Jing Tao and Xiao‐Hua Zhou

On identifiability and consistency of the nugget in Gaussian spatial process models pp. 1044-1070

Wenpin Tang, Lu Zhang and Sudipto Banerjee

Volume 83, issue 4, 2021

Inference on the history of a randomly growing tree pp. 639-668

Harry Crane and Min Xu

Optimal statistical inference for individualized treatment effects in high‐dimensional models pp. 669-719

Tianxi Cai, T. Tony Cai and Zijian Guo

Covariate powered cross‐weighted multiple testing pp. 720-751

Nikolaos Ignatiadis and Wolfgang Huber

The confidence interval method for selecting valid instrumental variables pp. 752-776

Frank Windmeijer, Xiaoran Liang, Fernando P. Hartwig and Jack Bowden

Leveraging the Fisher randomization test using confidence distributions: Inference, combination and fusion learning pp. 777-797

Xiaokang Luo, Tirthankar Dasgupta, Minge Xie and Regina Y. Liu

Spatial birth–death–move processes: Basic properties and estimation of their intensity functions pp. 798-825

Frédéric Lavancier and Ronan Le Guével

Joint quantile regression for spatial data pp. 826-852

Xu Chen and Surya T. Tokdar

Approximate Laplace approximations for scalable model selection pp. 853-879

David Rossell, Oriol Abril and Anirban Bhattacharya

Wang and Leng (2016), High‐dimensional ordinary least‐squares projection for screening variables, Journal of the Royal Statistical Society Series B, 78, 589–611 pp. 880-881

Xiangyu Wang, Chenlei Leng and Tom Boot

Errata to “Functional models for time‐varying random objects” pp. 883-883

Paromita Dubey and Hans‐Georg Müller

Volume 83, issue 3, 2021

Prior sample size extensions for assessing prior impact and prior‐likelihood discordance pp. 413-437

Matthew Reimherr, Xiao‐Li Meng and Dan L. Nicolae

Valid and approximately valid confidence intervals for current status data pp. 438-452

Sungwook Kim, Michael P. Fay and Michael A. Proschan

Variable selection with ABC Bayesian forests pp. 453-481

Yi Liu, Veronika Ročková and Yuexi Wang

Increasing power for observational studies of aberrant response: An adaptive approach pp. 482-504

Siyu Heng, Hyunseung Kang, Dylan S. Small and Colin B. Fogarty

AMF: Aggregated Mondrian forests for online learning pp. 505-533

Jaouad Mourtada, Stéphane Gaïffas and Erwan Scornet

GGM knockoff filter: False discovery rate control for Gaussian graphical models pp. 534-558

Jinzhou Li and Marloes H. Maathuis

Estimation of causal quantile effects with a binary instrumental variable and censored data pp. 559-578

Bo Wei, Limin Peng, Mei‐Jie Zhang and Jason P. Fine

Modelling high‐dimensional categorical data using nonconvex fusion penalties pp. 579-611

Benjamin G. Stokell, Rajen D. Shah and Ryan J. Tibshirani

Instrument residual estimator for any response variable with endogenous binary treatment pp. 612-635

Myoung-jae Lee

Volume 83, issue 2, 2021

Anchor regression: Heterogeneous data meet causality pp. 215-246

Dominik Rothenhäusler, Nicolai Meinshausen, Peter Bühlmann and Jonas Peters

Finite sample change point inference and identification for high‐dimensional mean vectors pp. 247-270

Mengjia Yu and Xiaohui Chen

Iterative Alpha Expansion for estimating gradient‐sparse signals from linear measurements pp. 271-292

Sheng Xu and Zhou Fan

Estimation and clustering in popularity adjusted block model pp. 293-317

Majid Noroozi, Ramchandra Rimal and Marianna Pensky

Estimating optimal treatment rules with an instrumental variable: A partial identification learning approach pp. 318-345

Hongming Pu and Bo Zhang

Nonparametric density estimation over complicated domains pp. 346-368

Federico Ferraccioli, Eleonora Arnone, Livio Finos, James O. Ramsay and Laura M. Sangalli

Principal manifold estimation via model complexity selection pp. 369-394

Kun Meng and Ani Eloyan

On optimal rerandomization designs pp. 395-403

Per Johansson, Donald B. Rubin and Mårten Schultzberg

On the optimality of randomization in experimental design: How to randomize for minimax variance and design‐based inference pp. 404-409

Nathan Kallus

Volume 83, issue 1, 2021

Report of the Editors—2020 pp. 3-4

Aurore Delaigle and Simon Wood

Construction of blocked factorial designs to estimate main effects and selected two‐factor interactions pp. 5-29

J. D. Godolphin

Use of model reparametrization to improve variational Bayes pp. 30-57

Linda S. L. Tan

Statistical inferences of linear forms for noisy matrix completion pp. 58-77

Dong Xia and Ming Yuan

Small area estimation with linked data pp. 78-107

N. Salvati, E. Fabrizi, M. G. Ranalli and R. L. Chambers

Smoothing splines on Riemannian manifolds, with applications to 3D shape space pp. 108-132

Kwang‐Rae Kim, Ian L. Dryden, Huiling Le and Katie E. Severn

Optimal control of false discovery criteria in the two‐group model pp. 133-155

Ruth Heller and Saharon Rosset

Gibbs flow for approximate transport with applications to Bayesian computation pp. 156-187

Jeremy Heng, Arnaud Doucet and Yvo Pokern

The proximal Robbins–Monro method pp. 188-212

Panos Toulis, Thibaut Horel and Edoardo M. Airoldi

Volume 82, issue 5, 2020

Quasi‐stationary Monte Carlo and the ScaLE algorithm pp. 1167-1221

Murray Pollock, Paul Fearnhead, Adam Johansen and Gareth O. Roberts

An information theoretic approach for selecting arms in clinical trials pp. 1223-1247

Pavel Mozgunov and Thomas Jaki

Estimating densities with non‐linear support by using Fisher–Gaussian kernels pp. 1249-1271

Minerva Mukhopadhyay, Didong Li and David B. Dunson

A simple new approach to variable selection in regression, with application to genetic fine mapping pp. 1273-1300

Gao Wang, Abhishek Sarkar, Peter Carbonetto and Matthew Stephens

Robust tests for treatment effect in survival analysis under covariate‐adaptive randomization pp. 1301-1323

Ting Ye and Jun Shao

Beta–negative binomial auto‐regressions for modelling integer‐valued time series with extreme observations pp. 1325-1347

Paolo Gorgi

Modified likelihood root in high dimensions pp. 1349-1369

Yanbo Tang and Nancy Reid

Spatiotemporal modelling using integro‐difference equations with bivariate stable kernels pp. 1371-1392

Robert Richardson, Athanasios Kottas and Bruno Sansó

Volume 82, issue 4, 2020

Graphical models for extremes pp. 871-932

Sebastian Engelke and Adrien S. Hitz

A unified data‐adaptive framework for high dimensional change point detection pp. 933-963

Bin Liu, Cheng Zhou, Xinsheng Zhang and Yufeng Liu

A scalable estimate of the out‐of‐sample prediction error via approximate leave‐one‐out cross‐validation pp. 965-996

Kamiar Rahnama Rad and Arian Maleki

False discovery and its control in low rank estimation pp. 997-1027

Armeen Taeb, Parikshit Shah and Venkat Chandrasekaran

Adaptive designs for optimal observed Fisher information pp. 1029-1058

Adam Lane

Visualizing the effects of predictor variables in black box supervised learning models pp. 1059-1086

Daniel W. Apley and Jingyu Zhu

Quasi‐Bayes properties of a procedure for sequential learning in mixture models pp. 1087-1114

Sandra Fortini and Sonia Petrone

Superconsistent estimation of points of impact in non‐parametric regression with functional predictors pp. 1115-1140

Dominik Poß, Dominik Liebl, Alois Kneip, Hedwig Eisenbarth, Tor D. Wager and Lisa Feldman Barrett

Optimal alpha spending for sequential analysis with binomial data pp. 1141-1164

Ivair R. Silva, Martin Kulldorff and W. Katherine Yih

Volume 82, issue 3, 2020

Unbiased Markov chain Monte Carlo methods with couplings pp. 543-600

Pierre E. Jacob, John O’Leary and Yves F. Atchadé

Robust estimation via robust gradient estimation pp. 601-627

Adarsh Prasad, Arun Sai Suggala, Sivaraman Balakrishnan and Pradeep Ravikumar

Testing relevant hypotheses in functional time series via self‐normalization pp. 629-660

Holger Dette, Kevin Kokot and Stanislav Volgushev

Causal mediation analysis for stochastic interventions pp. 661-683

Iván Díaz and Nima S. Hejazi

A flexible framework for hypothesis testing in high dimensions pp. 685-718

Adel Javanmard and Jason D. Lee

Causal isotonic regression pp. 719-747

Ted Westling, Peter Gilbert and Marco Carone

Optimal, two‐stage, adaptive enrichment designs for randomized trials, using sparse linear programming pp. 749-772

Michael Rosenblum, Ethan X. Fang and Han Liu

Goodness‐of‐fit testing in high dimensional generalized linear models pp. 773-795

Jana Janková, Rajen D. Shah, Peter Bühlmann and Richard J. Samworth

Inference for two‐stage sampling designs pp. 797-815

Guillaume Chauvet and Audrey‐Anne Vallée

On bandwidth choice for spatial data density estimation pp. 817-840

Zhenyu Jiang, Nengxiang Ling, Zudi Lu, Dag Tj⊘stheim and Qiang Zhang

Robust testing in generalized linear models by sign flipping score contributions pp. 841-864

Jesse Hemerik, Jelle J. Goeman and Livio Finos

Reply to the correction by Grover and Kaur: a new randomized response model pp. 865-868

Sarjinder Singh

Volume 82, issue 2, 2020

Functional models for time‐varying random objects pp. 275-327

Paromita Dubey and Hans‐Georg Müller

Sparse principal component analysis via axis‐aligned random projections pp. 329-359

Milana Gataric, Tengyao Wang and Richard J. Samworth

Right singular vector projection graphs: fast high dimensional covariance matrix estimation under latent confounding pp. 361-389

Rajen D. Shah, Benjamin Frot, Gian‐Andrea Thanei and Nicolai Meinshausen

Semisupervised inference for explained variance in high dimensional linear regression and its applications pp. 391-419

T. Tony Cai and Zijian Guo

Model misspecification in approximate Bayesian computation: consequences and diagnostics pp. 421-444

David T. Frazier, Christian P. Robert and Judith Rousseau

Doubly robust inference when combining probability and non‐probability samples with high dimensional data pp. 445-465

Shu Yang, Jae Kwang Kim and Rui Song

Sumca: simple, unified, Monte‐Carlo‐assisted approach to second‐order unbiased mean‐squared prediction error estimation pp. 467-485

Jiming Jiang and Mahmoud Torabi

Exchangeable random measures for sparse and modular graphs with overlapping communities pp. 487-520

Adrien Todeschini, Xenia Miscouridou and François Caron

Multiply robust causal inference with double‐negative control adjustment for categorical unmeasured confounding pp. 521-540

Xu Shi, Wang Miao, Jennifer C. Nelson and Eric J. Tchetgen Tchetgen

Volume 82, issue 1, 2020

Report of the Editors—2019 pp. 3-4

David Dunson and Simon Wood

Multiscale inference and long‐run variance estimation in non‐parametric regression with time series errors pp. 5-37

Marina Khismatullina and Michael Vogt

Making sense of sensitivity: extending omitted variable bias pp. 39-67

Carlos Cinelli and Chad Hazlett

Renewable estimation and incremental inference in generalized linear models with streaming data sets pp. 69-97

Lan Luo and Peter X.‐K. Song

Targeted sampling from massive block model graphs with personalized PageRank pp. 99-126

Fan Chen, Yini Zhang and Karl Rohe

A Bayesian hierarchical model for related densities by using Pólya trees pp. 127-153

Jonathan Christensen and Li Ma

Bayesian empirical likelihood inference with complex survey data pp. 155-174

Puying Zhao, Malay Ghosh, J. N. K. Rao and Changbao Wu

The conditional permutation test for independence while controlling for confounders pp. 175-197

Thomas B. Berrett, Yi Wang, Rina Foygel Barber and Richard J. Samworth

Robust inference on population indirect causal effects: the generalized front door criterion pp. 199-214

Isabel R. Fulcher, Ilya Shpitser, Stella Marealle and Eric J. Tchetgen Tchetgen

Multivariate type G Matérn stochastic partial differential equation random fields pp. 215-239

David Bolin and Jonas Wallin

Rerandomization and regression adjustment pp. 241-268

Xinran Li and Peng Ding

Correction: ‘A new randomized response model’ pp. 269-271

Lovleen Kumar Grover and Amanpreet Kaur