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November 2022, Volume 84, Issue 5
- 1589-1607 Modelling the COVID‐19 infection trajectory: A piecewise linear quantile trend model
by Feiyu Jiang & Zifeng Zhao & Xiaofeng Shao - 1608-1639 Calibrating the scan statistic: Finite sample performance versus asymptotics
by Guenther Walther & Andrew Perry - 1640-1665 General Bayesian loss function selection and the use of improper models
by Jack Jewson & David Rossell - 1666-1698 Exact clustering in tensor block model: Statistical optimality and computational limit
by Rungang Han & Yuetian Luo & Miaoyan Wang & Anru R. Zhang - 1699-1725 Segmenting time series via self‐normalisation
by Zifeng Zhao & Feiyu Jiang & Xiaofeng Shao - 1726-1750 An approximation algorithm for blocking of an experimental design
by Bikram Karmakar - 1751-1784 Dimension‐free mixing for high‐dimensional Bayesian variable selection
by Quan Zhou & Jun Yang & Dootika Vats & Gareth O. Roberts & Jeffrey S. Rosenthal - 1785-1820 CovNet: Covariance networks for functional data on multidimensional domains
by Soham Sarkar & Victor M. Panaretos - 1821-1850 Conditional independence testing in Hilbert spaces with applications to functional data analysis
by Anton Rask Lundborg & Rajen D. Shah & Jonas Peters - 1851-1885 Linear regression and its inference on noisy network‐linked data
by Can M. Le & Tianxi Li - 1886-1946 ZAP: Z$$ Z $$‐value adaptive procedures for false discovery rate control with side information
by Dennis Leung & Wenguang Sun - 1947-1968 Empirical likelihood‐based inference for functional means with application to wearable device data
by Hsin‐wen Chang & Ian W. McKeague - 1969-1999 Causal inference with spatio‐temporal data: Estimating the effects of airstrikes on insurgent violence in Iraq
by Georgia Papadogeorgou & Kosuke Imai & Jason Lyall & Fan Li - 2000-2031 High‐dimensional principal component analysis with heterogeneous missingness
by Ziwei Zhu & Tengyao Wang & Richard J. Samworth - 2032-2054 A statistical test to reject the structural interpretation of a latent factor model
by Tyler J. VanderWeele & Stijn Vansteelandt - 2055-2087 Structure learning for extremal tree models
by Sebastian Engelke & Stanislav Volgushev
September 2022, Volume 84, Issue 4
- 1059-1081 Optimal thinning of MCMC output
by Marina Riabiz & Wilson Ye Chen & Jon Cockayne & Pawel Swietach & Steven A. Niederer & Lester Mackey & Chris. J. Oates - 1082-1104 Testing for a change in mean after changepoint detection
by Sean Jewell & Paul Fearnhead & Daniela Witten - 1105-1128 Optimal and maximin procedures for multiple testing problems
by Saharon Rosset & Ruth Heller & Amichai Painsky & Ehud Aharoni - 1129-1149 Efficient manifold approximation with spherelets
by Didong Li & Minerva Mukhopadhyay & David B. Dunson - 1150-1174 Bootstrap inference for the finite population mean under complex sampling designs
by Zhonglei Wang & Liuhua Peng & Jae Kwang Kim - 1175-1197 Semiparametric latent class analysis of recurrent event data
by Wei Zhao & Limin Peng & John Hanfelt - 1198-1228 Fast increased fidelity samplers for approximate Bayesian Gaussian process regression
by Kelly R. Moran & Matthew W. Wheeler - 1229-1256 Manifold Markov chain Monte Carlo methods for Bayesian inference in diffusion models
by Matthew M. Graham & Alexandre H. Thiery & Alexandros Beskos - 1257-1286 Bayesian inference for risk minimization via exponentially tilted empirical likelihood
by Rong Tang & Yun Yang - 1287-1323 Bayesian context trees: Modelling and exact inference for discrete time series
by Ioannis Kontoyiannis & Lambros Mertzanis & Athina Panotopoulou & Ioannis Papageorgiou & Maria Skoularidou - 1324-1352 Nonparametric, tuning‐free estimation of S‐shaped functions
by Oliver Y. Feng & Yining Chen & Qiyang Han & Raymond J. Carroll & Richard J. Samworth - 1353-1391 Efficient evaluation of prediction rules in semi‐supervised settings under stratified sampling
by Jessica Gronsbell & Molei Liu & Lu Tian & Tianxi Cai - 1392-1422 Functional peaks‐over‐threshold analysis
by Raphaël de Fondeville & Anthony C. Davison - 1423-1445 Multiply robust estimation of causal effects under principal ignorability
by Zhichao Jiang & Shu Yang & Peng Ding - 1446-1473 A statistical interpretation of spectral embedding: The generalised random dot product graph
by Patrick Rubin‐Delanchy & Joshua Cape & Minh Tang & Carey E. Priebe - 1474-1502 On the cross‐validation bias due to unsupervised preprocessing
by Amit Moscovich & Saharon Rosset - 1503-1525 Paired or partially paired two‐sample tests with unordered samples
by Yudong Wang & Yanlin Tang & Zhi‐Sheng Ye - 1526-1557 The Debiased Spatial Whittle likelihood
by Arthur P. Guillaumin & Adam M. Sykulski & Sofia C. Olhede & Frederik J. Simons - 1558-1580 Universal prediction band via semi‐definite programming
by Tengyuan Liang - 1581-1585 Corrigendum to ‘Simulation of multivariate diffusion bridges’
by Mogens Bladt & Samuel Finch & Michael Sørensen
July 2022, Volume 84, Issue 3
- 657-685 Assumption‐lean inference for generalised linear model parameters
by Stijn Vansteelandt & Oliver Dukes - 686-689 Proposer of the vote of thanks and contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes
by Rhian M. Daniel - 689-691 Seconder of the vote of thanks to Vansteelandt and Dukes and contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’
by Vanessa Didelez - 691-693 Peng Ding’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes
by Peng Ding - 694-696 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
by Mats J. Stensrud & Aaron L. Sarvet - 696-698 Heather Battey’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes
by Heather Battey - 698-699 Christian Hennig's contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes
by Christian Hennig - 700-701 Pallavi Basuʼs contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes
by Pallavi Basu - 701-702 Blair Bilodeau's contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes
by Blair Bilodeau - 703-705 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
by Andreas Buja & Richard A. Berk & Arun K. Kuchibhotla & Linda Zhao & Ed George - 705-706 Anna Choi and Weng Kee Wong’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes
by Anna Choi & Weng Kee Wong - 707-708 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
by Chaohua Dong & Jiti Gao & Oliver Linton - 709-710 Oliver Hines and Karla Diaz‐Ordazʼs contribution to the discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes
by Oliver Hines & Karla Diaz‐Ordaz - 711-712 Ian Hunt's contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes
by Ian Hunt - 712-713 Kuldeep Kumar’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes
by Kuldeep Kumar - 713-714 Michael Lavine and James Hodges’ contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes
by Michael Lavine & James Hodges - 715-716 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
by Elizabeth L. Ogburn & Junhui Cai & Arun K. Kuchibhotla & Richard A. Berk & Andreas Buja - 717-718 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
by Rachael V. Phillips & Mark J. van der Laan - 719-720 Thomas S. Richardson’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes
by Thomas S. Richardson - 720-721 Ilya Shpitser’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes
by Ilya Shpitser - 722-723 Yanbo Tang's contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes
by Yanbo Tang - 723-725 Eric J Tchetgen Tchetgen’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes
by Eric J. Tchetgen Tchetgen - 725-726 Jiwei Zhao’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes
by Jiwei Zhao - 727-729 Niwen Zhou and Xu Guo’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes
by Niwen Zhou & Xu Guo - 729-739 Authors' reply to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes
by Stijn Vansteelandt & Oliver Dukes - 740-764 Bayesian estimation and comparison of conditional moment models
by Siddhartha Chib & Minchul Shin & Anna Simoni - 765-793 Statistical inference of the value function for reinforcement learning in infinite‐horizon settings
by Chengchun Shi & Sheng Zhang & Wenbin Lu & Rui Song - 794-821 Semiparametric estimation for causal mediation analysis with multiple causally ordered mediators
by Xiang Zhou - 822-852 False discovery rate control with e‐values
by Ruodu Wang & Aaditya Ramdas - 853-878 Empirical Bayes PCA in high dimensions
by Xinyi Zhong & Chang Su & Zhou Fan - 879-911 The sceptical Bayes factor for the assessment of replication success
by Samuel Pawel & Leonhard Held - 912-932 Supervised multivariate learning with simultaneous feature auto‐grouping and dimension reduction
by Yiyuan She & Jiahui Shen & Chao Zhang - 933-972 On functional processes with multiple discontinuities
by Jialiang Li & Yaguang Li & Tailen Hsing - 973-996 Coupling‐based convergence assessment of some Gibbs samplers for high‐dimensional Bayesian regression with shrinkage priors
by Niloy Biswas & Anirban Bhattacharya & Pierre E. Jacob & James E. Johndrow - 997-1022 Robust generalised Bayesian inference for intractable likelihoods
by Takuo Matsubara & Jeremias Knoblauch & François‐Xavier Briol & Chris J. Oates - 1023-1055 High‐dimensional changepoint estimation with heterogeneous missingness
by Bertille Follain & Tengyao Wang & Richard J. Samworth
April 2022, Volume 84, Issue 2
- 269-294 On efficient dimension reduction with respect to the interaction between two response variables
by Wei Luo - 295-320 Gaussian prepivoting for finite population causal inference
by Peter L. Cohen & Colin B. Fogarty - 321-350 Non‐reversible parallel tempering: A scalable highly parallel MCMC scheme
by Saifuddin Syed & Alexandre Bouchard‐Côté & George Deligiannidis & Arnaud Doucet - 351-381 Synthetic controls with staggered adoption
by Eli Ben‐Michael & Avi Feller & Jesse Rothstein - 382-413 Selective inference for effect modification via the lasso
by Qingyuan Zhao & Dylan S. Small & Ashkan Ertefaie - 414-439 Graph based Gaussian processes on restricted domains
by David B. Dunson & Hau‐Tieng Wu & Nan Wu - 440-472 Efficient learning of optimal individualized treatment rules for heteroscedastic or misspecified treatment‐free effect models
by Weibin Mo & Yufeng Liu - 473-495 Model identification via total Frobenius norm of multivariate spectra
by Tucker S. McElroy & Anindya Roy - 496-523 The Barker proposal: Combining robustness and efficiency in gradient‐based MCMC
by Samuel Livingstone & Giacomo Zanella - 524-546 Prediction and outlier detection in classification problems
by Leying Guan & Robert Tibshirani - 547-578 A kernel‐expanded stochastic neural network
by Yan Sun & Faming Liang - 579-599 Graphical criteria for efficient total effect estimation via adjustment in causal linear models
by Leonard Henckel & Emilija Perković & Marloes H. Maathuis - 600-629 Functional structural equation model
by Kuang‐Yao Lee & Lexin Li - 630-653 SIMPLE: Statistical inference on membership profiles in large networks
by Jianqing Fan & Yingying Fan & Xiao Han & Jinchi Lv
February 2022, Volume 84, Issue 1
- 3-37 Gaussian differential privacy
by Jinshuo Dong & Aaron Roth & Weijie J. Su - 37-38 Proposer of the vote of thanks to Dong et al. and contribution to the Discussion of ‘Gaussian Differential Privacy’
by Borja Balle - 39-41 Seconder of the vote of thanks to Dong et al. and contribution to the Discussion of ‘Gaussian Differential Privacy’
by Marco Avella‐Medina - 42-43 Peter Krusche and Frank Bretz's contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al
by Peter Krusche & Frank Bretz - 43-44 Christine P. Chai's contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al
by Christine P. Chai - 44-45 Sebastian Dietz’s contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al
by Sebastian Dietz - 46-47 J. Goseling and M.N.M. van Lieshout's contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al
by J. Goseling & M.N.M. van Lieshout - 47-48 Jorge Mateu’s contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al
by Jorge Mateu - 49-50 Priyantha Wijayatunga’s contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al
by Priyantha Wijayatunga - 50-54 Authors’ reply to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al
by Jinshuo Dong & Aaron Roth & Weijie J. Su - 55-82 Usable and precise asymptotics for generalized linear mixed model analysis and design
by Jiming Jiang & Matt P. Wand & Aishwarya Bhaskaran - 83-113 Inferential Wasserstein generative adversarial networks
by Yao Chen & Qingyi Gao & Xiao Wang - 114-148 Waste‐free sequential Monte Carlo
by Hai‐Dang Dau & Nicolas Chopin - 149-173 Transfer learning for high‐dimensional linear regression: Prediction, estimation and minimax optimality
by Sai Li & T. Tony Cai & Hongzhe Li - 174-204 A graph‐theoretic approach to randomization tests of causal effects under general interference
by David Puelz & Guillaume Basse & Avi Feller & Panos Toulis - 205-233 High‐dimensional quantile regression: Convolution smoothing and concave regularization
by Kean Ming Tan & Lan Wang & Wen‐Xin Zhou - 234-266 High‐dimensional, multiscale online changepoint detection
by Yudong Chen & Tengyao Wang & Richard J. Samworth
November 2021, Volume 83, Issue 5
- 887-910 Analysis of networks via the sparse β‐model
by Mingli Chen & Kengo Kato & Chenlei Leng - 911-938 Conformal inference of counterfactuals and individual treatment effects
by Lihua Lei & Emmanuel J. Candès - 939-962 Two‐sample inference for high‐dimensional Markov networks
by Byol Kim & Song Liu & Mladen Kolar - 963-993 Isotonic distributional regression
by Alexander Henzi & Johanna F. Ziegel & Tilmann Gneiting - 994-1015 Model‐assisted analyses of cluster‐randomized experiments
by Fangzhou Su & Peng Ding - 1016-1043 Inference of heterogeneous treatment effects using observational data with high‐dimensional covariates
by Yumou Qiu & Jing Tao & Xiao‐Hua Zhou - 1044-1070 On identifiability and consistency of the nugget in Gaussian spatial process models
by Wenpin Tang & Lu Zhang & Sudipto Banerjee
September 2021, Volume 83, Issue 4
- 639-668 Inference on the history of a randomly growing tree
by Harry Crane & Min Xu - 669-719 Optimal statistical inference for individualized treatment effects in high‐dimensional models
by Tianxi Cai & T. Tony Cai & Zijian Guo - 720-751 Covariate powered cross‐weighted multiple testing
by Nikolaos Ignatiadis & Wolfgang Huber - 752-776 The confidence interval method for selecting valid instrumental variables
by Frank Windmeijer & Xiaoran Liang & Fernando P. Hartwig & Jack Bowden - 777-797 Leveraging the Fisher randomization test using confidence distributions: Inference, combination and fusion learning
by Xiaokang Luo & Tirthankar Dasgupta & Minge Xie & Regina Y. Liu - 798-825 Spatial birth–death–move processes: Basic properties and estimation of their intensity functions
by Frédéric Lavancier & Ronan Le Guével - 826-852 Joint quantile regression for spatial data
by Xu Chen & Surya T. Tokdar - 853-879 Approximate Laplace approximations for scalable model selection
by David Rossell & Oriol Abril & Anirban Bhattacharya - 880-881 Wang and Leng (2016), High‐dimensional ordinary least‐squares projection for screening variables, Journal of the Royal Statistical Society Series B, 78, 589–611
by Xiangyu Wang & Chenlei Leng & Tom Boot - 883-883 Errata to “Functional models for time‐varying random objects”
by Paromita Dubey & Hans‐Georg Müller
July 2021, Volume 83, Issue 3
- 413-437 Prior sample size extensions for assessing prior impact and prior‐likelihood discordance
by Matthew Reimherr & Xiao‐Li Meng & Dan L. Nicolae - 438-452 Valid and approximately valid confidence intervals for current status data
by Sungwook Kim & Michael P. Fay & Michael A. Proschan - 453-481 Variable selection with ABC Bayesian forests
by Yi Liu & Veronika Ročková & Yuexi Wang - 482-504 Increasing power for observational studies of aberrant response: An adaptive approach
by Siyu Heng & Hyunseung Kang & Dylan S. Small & Colin B. Fogarty - 505-533 AMF: Aggregated Mondrian forests for online learning
by Jaouad Mourtada & Stéphane Gaïffas & Erwan Scornet - 534-558 GGM knockoff filter: False discovery rate control for Gaussian graphical models
by Jinzhou Li & Marloes H. Maathuis - 559-578 Estimation of causal quantile effects with a binary instrumental variable and censored data
by Bo Wei & Limin Peng & Mei‐Jie Zhang & Jason P. Fine - 579-611 Modelling high‐dimensional categorical data using nonconvex fusion penalties
by Benjamin G. Stokell & Rajen D. Shah & Ryan J. Tibshirani - 612-635 Instrument residual estimator for any response variable with endogenous binary treatment
by Myoung‐jae Lee
April 2021, Volume 83, Issue 2
- 215-246 Anchor regression: Heterogeneous data meet causality
by Dominik Rothenhäusler & Nicolai Meinshausen & Peter Bühlmann & Jonas Peters - 247-270 Finite sample change point inference and identification for high‐dimensional mean vectors
by Mengjia Yu & Xiaohui Chen - 271-292 Iterative Alpha Expansion for estimating gradient‐sparse signals from linear measurements
by Sheng Xu & Zhou Fan - 293-317 Estimation and clustering in popularity adjusted block model
by Majid Noroozi & Ramchandra Rimal & Marianna Pensky - 318-345 Estimating optimal treatment rules with an instrumental variable: A partial identification learning approach
by Hongming Pu & Bo Zhang - 346-368 Nonparametric density estimation over complicated domains
by Federico Ferraccioli & Eleonora Arnone & Livio Finos & James O. Ramsay & Laura M. Sangalli - 369-394 Principal manifold estimation via model complexity selection
by Kun Meng & Ani Eloyan - 395-403 On optimal rerandomization designs
by Per Johansson & Donald B. Rubin & Mårten Schultzberg - 404-409 On the optimality of randomization in experimental design: How to randomize for minimax variance and design‐based inference
by Nathan Kallus
February 2021, Volume 83, Issue 1
- 3-4 Report of the Editors—2020
by Aurore Delaigle & Simon Wood - 5-29 Construction of blocked factorial designs to estimate main effects and selected two‐factor interactions
by J. D. Godolphin - 30-57 Use of model reparametrization to improve variational Bayes
by Linda S. L. Tan - 58-77 Statistical inferences of linear forms for noisy matrix completion
by Dong Xia & Ming Yuan - 78-107 Small area estimation with linked data
by N. Salvati & E. Fabrizi & M. G. Ranalli & R. L. Chambers - 108-132 Smoothing splines on Riemannian manifolds, with applications to 3D shape space
by Kwang‐Rae Kim & Ian L. Dryden & Huiling Le & Katie E. Severn - 133-155 Optimal control of false discovery criteria in the two‐group model
by Ruth Heller & Saharon Rosset - 156-187 Gibbs flow for approximate transport with applications to Bayesian computation
by Jeremy Heng & Arnaud Doucet & Yvo Pokern - 188-212 The proximal Robbins–Monro method
by Panos Toulis & Thibaut Horel & Edoardo M. Airoldi
December 2020, Volume 82, Issue 5
- 1167-1221 Quasi‐stationary Monte Carlo and the ScaLE algorithm
by Murray Pollock & Paul Fearnhead & Adam M. Johansen & Gareth O. Roberts - 1223-1247 An information theoretic approach for selecting arms in clinical trials
by Pavel Mozgunov & Thomas Jaki - 1249-1271 Estimating densities with non‐linear support by using Fisher–Gaussian kernels
by Minerva Mukhopadhyay & Didong Li & David B. Dunson - 1273-1300 A simple new approach to variable selection in regression, with application to genetic fine mapping
by Gao Wang & Abhishek Sarkar & Peter Carbonetto & Matthew Stephens - 1301-1323 Robust tests for treatment effect in survival analysis under covariate‐adaptive randomization
by Ting Ye & Jun Shao - 1325-1347 Beta–negative binomial auto‐regressions for modelling integer‐valued time series with extreme observations
by Paolo Gorgi - 1349-1369 Modified likelihood root in high dimensions
by Yanbo Tang & Nancy Reid - 1371-1392 Spatiotemporal modelling using integro‐difference equations with bivariate stable kernels
by Robert Richardson & Athanasios Kottas & Bruno Sansó
September 2020, Volume 82, Issue 4
- 871-932 Graphical models for extremes
by Sebastian Engelke & Adrien S. Hitz - 933-963 A unified data‐adaptive framework for high dimensional change point detection
by Bin Liu & Cheng Zhou & Xinsheng Zhang & Yufeng Liu - 965-996 A scalable estimate of the out‐of‐sample prediction error via approximate leave‐one‐out cross‐validation
by Kamiar Rahnama Rad & Arian Maleki - 997-1027 False discovery and its control in low rank estimation
by Armeen Taeb & Parikshit Shah & Venkat Chandrasekaran - 1029-1058 Adaptive designs for optimal observed Fisher information
by Adam Lane - 1059-1086 Visualizing the effects of predictor variables in black box supervised learning models
by Daniel W. Apley & Jingyu Zhu - 1087-1114 Quasi‐Bayes properties of a procedure for sequential learning in mixture models
by Sandra Fortini & Sonia Petrone - 1115-1140 Superconsistent estimation of points of impact in non‐parametric regression with functional predictors
by Dominik Poß & Dominik Liebl & Alois Kneip & Hedwig Eisenbarth & Tor D. Wager & Lisa Feldman Barrett - 1141-1164 Optimal alpha spending for sequential analysis with binomial data
by Ivair R. Silva & Martin Kulldorff & W. Katherine Yih
July 2020, Volume 82, Issue 3
- 543-600 Unbiased Markov chain Monte Carlo methods with couplings
by Pierre E. Jacob & John O’Leary & Yves F. Atchadé - 601-627 Robust estimation via robust gradient estimation
by Adarsh Prasad & Arun Sai Suggala & Sivaraman Balakrishnan & Pradeep Ravikumar - 629-660 Testing relevant hypotheses in functional time series via self‐normalization
by Holger Dette & Kevin Kokot & Stanislav Volgushev - 661-683 Causal mediation analysis for stochastic interventions
by Iván Díaz & Nima S. Hejazi - 685-718 A flexible framework for hypothesis testing in high dimensions
by Adel Javanmard & Jason D. Lee - 719-747 Causal isotonic regression
by Ted Westling & Peter Gilbert & Marco Carone - 749-772 Optimal, two‐stage, adaptive enrichment designs for randomized trials, using sparse linear programming
by Michael Rosenblum & Ethan X. Fang & Han Liu - 773-795 Goodness‐of‐fit testing in high dimensional generalized linear models
by Jana Janková & Rajen D. Shah & Peter Bühlmann & Richard J. Samworth - 797-815 Inference for two‐stage sampling designs
by Guillaume Chauvet & Audrey‐Anne Vallée - 817-840 On bandwidth choice for spatial data density estimation
by Zhenyu Jiang & Nengxiang Ling & Zudi Lu & Dag Tj⊘stheim & Qiang Zhang - 841-864 Robust testing in generalized linear models by sign flipping score contributions
by Jesse Hemerik & Jelle J. Goeman & Livio Finos - 865-868 Reply to the correction by Grover and Kaur: a new randomized response model
by Sarjinder Singh
April 2020, Volume 82, Issue 2
- 275-327 Functional models for time‐varying random objects
by Paromita Dubey & Hans‐Georg Müller - 329-359 Sparse principal component analysis via axis‐aligned random projections
by Milana Gataric & Tengyao Wang & Richard J. Samworth - 361-389 Right singular vector projection graphs: fast high dimensional covariance matrix estimation under latent confounding
by Rajen D. Shah & Benjamin Frot & Gian‐Andrea Thanei & Nicolai Meinshausen - 391-419 Semisupervised inference for explained variance in high dimensional linear regression and its applications
by T. Tony Cai & Zijian Guo - 421-444 Model misspecification in approximate Bayesian computation: consequences and diagnostics
by David T. Frazier & Christian P. Robert & Judith Rousseau - 445-465 Doubly robust inference when combining probability and non‐probability samples with high dimensional data
by Shu Yang & Jae Kwang Kim & Rui Song - 467-485 Sumca: simple, unified, Monte‐Carlo‐assisted approach to second‐order unbiased mean‐squared prediction error estimation
by Jiming Jiang & Mahmoud Torabi - 487-520 Exchangeable random measures for sparse and modular graphs with overlapping communities
by Adrien Todeschini & Xenia Miscouridou & François Caron - 521-540 Multiply robust causal inference with double‐negative control adjustment for categorical unmeasured confounding
by Xu Shi & Wang Miao & Jennifer C. Nelson & Eric J. Tchetgen Tchetgen
February 2020, Volume 82, Issue 1
- 3-4 Report of the Editors—2019
by David Dunson & Simon Wood - 5-37 Multiscale inference and long‐run variance estimation in non‐parametric regression with time series errors
by Marina Khismatullina & Michael Vogt - 39-67 Making sense of sensitivity: extending omitted variable bias
by Carlos Cinelli & Chad Hazlett - 69-97 Renewable estimation and incremental inference in generalized linear models with streaming data sets
by Lan Luo & Peter X.‐K. Song - 99-126 Targeted sampling from massive block model graphs with personalized PageRank
by Fan Chen & Yini Zhang & Karl Rohe - 127-153 A Bayesian hierarchical model for related densities by using Pólya trees
by Jonathan Christensen & Li Ma - 155-174 Bayesian empirical likelihood inference with complex survey data
by Puying Zhao & Malay Ghosh & J. N. K. Rao & Changbao Wu - 175-197 The conditional permutation test for independence while controlling for confounders
by Thomas B. Berrett & Yi Wang & Rina Foygel Barber & Richard J. Samworth - 199-214 Robust inference on population indirect causal effects: the generalized front door criterion
by Isabel R. Fulcher & Ilya Shpitser & Stella Marealle & Eric J. Tchetgen Tchetgen - 215-239 Multivariate type G Matérn stochastic partial differential equation random fields
by David Bolin & Jonas Wallin - 241-268 Rerandomization and regression adjustment
by Xinran Li & Peng Ding - 269-271 Correction: ‘A new randomized response model’
by Lovleen Kumar Grover & Amanpreet Kaur
November 2019, Volume 81, Issue 5
- 809-837 On choosing mixture components via non‐local priors
by Jairo Fúquene & Mark Steel & David Rossell - 839-860 Fused density estimation: theory and methods
by Robert Bassett & James Sharpnack