Journal of the Royal Statistical Society Series B-Statistical Methodology最新文献

Principal stratification with U-statistics under principal ignorability. 主可忽略性下u统计量的主分层。

IF 3.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2026-05-27 DOI: 10.1093/jrsssb/qkag044

Xinyuan Chen, Fan Li

{"title":"Principal stratification with U-statistics under principal ignorability.","authors":"Xinyuan Chen, Fan Li","doi":"10.1093/jrsssb/qkag044","DOIUrl":"10.1093/jrsssb/qkag044","url":null,"abstract":"Principal stratification is a popular framework for causal inference in the presence of an intermediate outcome. While the principal average treatment effects are the standard target of inference, they may be insufficient when interest lies in the relative ordering of potential outcomes within a principal stratum. We introduce the principal generalized causal effect estimands to accommodate nonlinear contrast functions, providing robust, probability-scale summaries suitable for ordinal outcomes and win-loss comparisons with composite endpoints. Under principal ignorability, we expand the theoretical results in Jiang et al. (J R Stat Soc Series B., 2022, 84(4), 1423-1445) to a broader class of causal estimands in the presence of a binary intermediate variable. We develop nonparametric identification results and derive efficient influence functions for the generalized causal estimands in principal stratification analyses. These efficient influence functions motivate multiply robust estimators and lay the ground for obtaining efficient debiased machine learning estimators via cross-fitting based on U-statistics. The proposed methods are illustrated through simulations and the analysis of a data example.","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13307717/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148347168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Causal K-Means Clustering. 因果k -均值聚类。

IF 3.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2026-04-27 DOI: 10.1093/jrsssb/qkag068

Kwangho Kim, Jisu Kim, Edward H Kennedy

{"title":"Causal K-Means Clustering.","authors":"Kwangho Kim, Jisu Kim, Edward H Kennedy","doi":"10.1093/jrsssb/qkag068","DOIUrl":"10.1093/jrsssb/qkag068","url":null,"abstract":"Causal effects are often characterized with population summaries. These might provide an incomplete picture when there are heterogeneous treatment effects across subgroups. Since the subgroup structure is typically unknown, it is more challenging to identify and evaluate subgroup effects than population effects. We propose a new solution to this problem: Causal k-Means Clustering, which leverages the k-means clustering algorithm to uncover the unknown subgroup structure. Our problem differs significantly from the conventional clustering setup since the variables to be clustered are unknown counterfactual functions. We present a plug-in estimator which is simple and readily implementable using off-the-shelf algorithms, and study its rate of convergence. We also develop a new bias-corrected estimator based on nonparametric efficiency theory and double machine learning, and show that this estimator achieves fast root-n rates and asymptotic normality in large nonparametric models. Our proposed methods are especially useful for modern outcome-wide studies with multiple treatment levels. Further, our framework is extensible to clustering with generic pseudo-outcomes, such as partially observed outcomes or otherwise unknown functions. Finally, we explore finite sample properties via simulation, and illustrate the proposed methods using a study of mobile-supported self-management for chronic low back pain.","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13132527/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147823022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inference on function-valued parameters using a restricted score test. 使用受限分数测试对函数值参数的推断。

IF 3.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2026-02-10 DOI: 10.1093/jrsssb/qkag043

Aaron Hudson, Marco Carone, Ali Shojaie

{"title":"Inference on function-valued parameters using a restricted score test.","authors":"Aaron Hudson, Marco Carone, Ali Shojaie","doi":"10.1093/jrsssb/qkag043","DOIUrl":"10.1093/jrsssb/qkag043","url":null,"abstract":"It is often of interest to make inference on an unknown function that is a local parameter of the data-generating mechanism, such as a density or regression function. Such estimands can typically only be estimated at a slower-than-parametric rate in nonparametric and semiparametric models, and performing calibrated inference can be challenging. In many cases, these estimands can be expressed as the minimizer of a population risk functional. Here, we propose a general framework that leverages such representation and provides a nonparametric extension of the score test for inference on an infinite-dimensional risk minimizer. We demonstrate that our framework is applicable in a wide variety of problems. As both analytic and computational examples, we describe how to use our general approach for inference on a mean regression function under (i) nonparametric and (ii) partially additive models, and evaluate the operating characteristics of the resulting procedures via simulations. Assessment of effect heterogeneity, inference on density functions, and conditional independence testing are discussed as additional examples.","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12971076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147437033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simplifying debiased inference via automatic differentiation and probabilistic programming. 通过自动微分和概率规划简化无偏差推理。

IF 3.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2026-02-01 Epub Date: 2025-09-10 DOI: 10.1093/jrsssb/qkaf052

Alex Luedtke

引用次数: 0

Pretraining and the lasso. 预训练和套索。

IF 3.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2026-02-01 Epub Date: 2025-08-08 DOI: 10.1093/jrsssb/qkaf050

Erin Craig, Mert Pilanci, Thomas Le Menestrel, Balasubramanian Narasimhan, Manuel A Rivas, Stein-Erik Gullaksen, Roozbeh Dehghannasiri, Julia Salzman, Jonathan Taylor, Robert Tibshirani

{"title":"Pretraining and the lasso.","authors":"Erin Craig, Mert Pilanci, Thomas Le Menestrel, Balasubramanian Narasimhan, Manuel A Rivas, Stein-Erik Gullaksen, Roozbeh Dehghannasiri, Julia Salzman, Jonathan Taylor, Robert Tibshirani","doi":"10.1093/jrsssb/qkaf050","DOIUrl":"https://doi.org/10.1093/jrsssb/qkaf050","url":null,"abstract":"Pre-training is a powerful paradigm in machine learning to pass information across models. For example, suppose one has a modest-sized dataset of images of cats and dogs and plans to fit a deep neural network to classify them. With pre-training, we start with a neural network trained on a large corpus of images of not just cats and dogs but hundreds of classes. We fix all network weights except the top layer(s) and fine tune on our dataset. This often results in dramatically better performance than training solely on our dataset. Here, we ask: 'Can pre-training help the lasso?'. We propose a framework where the lasso is fit on a large dataset and then fine-tuned on a smaller dataset. The latter can be a subset of the original, or have a different but related outcome. This framework has a wide variety of applications, including stratified and multi-response models. In the stratified model setting, lasso pre-training first estimates coefficients common to all groups, then estimates group-specific coefficients during fine-tuning. Under appropriate assumptions, support recovery of the common coefficients is superior to the usual lasso trained on individual groups. This separate identification of common and individual coefficients also aids scientific understanding.","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":"88 1","pages":"261-281"},"PeriodicalIF":3.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13472185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148765695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Harmonized Estimation of Subgroup-Specific Treatment Effects in Randomized Trials: The Use of External Control Data. 随机试验中亚组特异性治疗效果的统一估计：外部对照数据的使用。

IF 3.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2026-02-01 Epub Date: 2025-07-24 DOI: 10.1093/jrsssb/qkaf045

Daniel Schwartz, Riddhiman Saha, Steffen Ventz, Lorenzo Trippa

{"title":"Harmonized Estimation of Subgroup-Specific Treatment Effects in Randomized Trials: The Use of External Control Data.","authors":"Daniel Schwartz, Riddhiman Saha, Steffen Ventz, Lorenzo Trippa","doi":"10.1093/jrsssb/qkaf045","DOIUrl":"10.1093/jrsssb/qkaf045","url":null,"abstract":"Subgroup analyses of randomized controlled trials (RCTs) constitute an important component of the drug development process in precision medicine. In particular, subgroup analyses of early-stage trials often influence the design and eligibility criteria of subsequent confirmatory trials and ultimately influence which subpopulations will receive the treatment after regulatory approval. However, subgroup analyses are often complicated by small sample sizes, which leads to substantial uncertainty about subgroup-specific treatment effects. We explore the use of external control (EC) data to augment RCT subgroup analyses. We define and discuss harmonized estimators of subpopulation-specific treatment effects that leverage EC data. Our approach can be used to modify any subgroup-specific treatment effect estimates that are obtained by combining RCT and EC data, such as linear regression. We alter these subgroup-specific estimates to make them coherent with a robust estimate of the average effect in the randomized population based only on RCT data. The weighted average of the resulting subgroup-specific harmonized estimates matches the RCT-only estimate of the overall effect in the randomized population. We discuss the proposed harmonized estimators through analytic results and simulations, and investigate standard performance metrics. The method is illustrated with a case study in oncology.","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":"88 1","pages":"143-170"},"PeriodicalIF":3.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13042083/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147610566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Federated feature selection with false discovery rate control. 具有错误发现率控制的联邦特征选择。

IF 3.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2025-12-23 eCollection Date: 2026-07-01 DOI: 10.1093/jrsssb/qkaf074

Jie Hu, Jiayi Tong, Yang Ning, Cheng Yong Tang, Jason H Moore, Runze Li, Yong Chen

{"title":"Federated feature selection with false discovery rate control.","authors":"Jie Hu, Jiayi Tong, Yang Ning, Cheng Yong Tang, Jason H Moore, Runze Li, Yong Chen","doi":"10.1093/jrsssb/qkaf074","DOIUrl":"10.1093/jrsssb/qkaf074","url":null,"abstract":"Selecting a set of universally relevant features associated with a given response variable across multiple distributed data sites is an important problem in numerous scientific fields. However, performing this federated feature selection task becomes challenging when individual-level data cannot be shared due to privacy concerns. The problem is further complicated by potential heterogeneity in both feature distributions and model parameters across sites. In this paper, we propose Fed-false discovery rate (FDR), a federated feature selection framework that simultaneously identifies important features while controlling the FDR. To ensure privacy preservation and reduce communication costs, the Fed-FDR shares only lower-dimensional coefficient estimates instead of transmitting summary statistics for all features, with the dimensionality shown to be of the same order as the number of relevant features. The coordinating centre then leverages these lower-dimensional coefficient estimates to construct a generalized mirror statistic to identify the important features. The Fed-FDR is robust to the heterogeneity of feature distribution and model parameters, easy to implement, and computationally efficient. We further demonstrate that Fed-FDR effectively controls the FDR while achieving strong statistical power in our simulation studies. The results of the empirical study also demonstrate that the method is both valid and implementation-ready.","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":"88 3","pages":"978-997"},"PeriodicalIF":3.8,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13370347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-task learning for sparsity pattern heterogeneity: statistical and computational perspectives. 稀疏模式异质性的多任务学习：统计和计算视角。

IF 3.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2025-12-17 eCollection Date: 2026-07-01 DOI: 10.1093/jrsssb/qkaf076

Kayhan Behdin, Gabriel Loewinger, Kenneth T Kishida, Giovanni Parmigiani, Rahul Mazumder

{"title":"Multi-task learning for sparsity pattern heterogeneity: statistical and computational perspectives.","authors":"Kayhan Behdin, Gabriel Loewinger, Kenneth T Kishida, Giovanni Parmigiani, Rahul Mazumder","doi":"10.1093/jrsssb/qkaf076","DOIUrl":"10.1093/jrsssb/qkaf076","url":null,"abstract":"We consider a problem in multi-task learning (MTL) where multiple linear models are jointly trained on a collection of datasets ('tasks'). A key novelty of our framework is that it allows the sparsity pattern of regression coefficients and the values of non-zero coefficients to differ across tasks while still leveraging partially shared structure. Our methods encourage models to share information across tasks through separately encouraging (1) coefficient supports, and/or (2) nonzero coefficient values to be similar. This allows models to borrow strength during variable selection even when non-zero coefficient values differ across tasks. We propose a novel mixed-integer programming formulation for our estimator. We develop custom scalable algorithms based on block coordinate descent and combinatorial local search to obtain high-quality (approximate) solutions for our estimator. Additionally, we propose a novel exact optimization algorithm to obtain globally optimal solutions. We investigate the theoretical properties of our estimators. We formally show how our estimators leverage the shared support information across tasks to achieve better variable selection performance. We evaluate the performance of our methods in simulations and two biomedical applications. Our proposed approaches appear to outperform other sparse MTL methods in variable selection and prediction accuracy. We provide the sMTL package on CRAN.","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":"88 3","pages":"1029-1059"},"PeriodicalIF":3.8,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13370345/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Minimax and adaptive transfer learning for nonparametric classification under distributed differential privacy constraints. 分布式差分隐私约束下非参数分类的极大极小自适应迁移学习。

IF 3.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2025-11-12 eCollection Date: 2026-07-01 DOI: 10.1093/jrsssb/qkaf070

Arnab Auddy, T Tony Cai, Abhinav Chakraborty

{"title":"Minimax and adaptive transfer learning for nonparametric classification under distributed differential privacy constraints.","authors":"Arnab Auddy, T Tony Cai, Abhinav Chakraborty","doi":"10.1093/jrsssb/qkaf070","DOIUrl":"10.1093/jrsssb/qkaf070","url":null,"abstract":"This paper considers minimax and adaptive transfer learning for nonparametric classification under the posterior drift model with distributed differential privacy constraints. Our study is conducted within a heterogeneous framework, encompassing diverse sample sizes, varying privacy parameters, and data heterogeneity across different servers. We first establish the minimax misclassification rate, precisely characterizing the effects of privacy constraints, source samples, and target samples on classification accuracy. The results reveal interesting phase transition phenomena and highlight the intricate trade-offs between preserving privacy and achieving classification accuracy. We then develop a data-driven adaptive classifier that achieves the optimal rate within a logarithmic factor across a large collection of parameter spaces while satisfying the same set of differential privacy constraints. Simulation studies and real-world data applications further elucidate the theoretical analysis with numerical results.","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":"88 3","pages":"903-929"},"PeriodicalIF":3.8,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13370346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction to: Inference of dependency knowledge graph for Electronic Health Records. 更正：电子健康记录依赖关系知识图谱的推断。

IF 3.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2025-11-04 eCollection Date: 2026-04-01 DOI: 10.1093/jrsssb/qkaf071

引用次数: 0