Electronic Journal of Statistics最新文献

Distributed robust estimation and inference with contaminated data. 基于污染数据的分布式鲁棒估计与推断。

IF 1.5 4区数学

Electronic Journal of Statistics Pub Date : 2026-01-01 Epub Date: 2026-02-05 DOI: 10.1214/26-EJS2492

Peiliang Zhang, Wen-Xin Zhou, Zhao Ren

{"title":"Distributed robust estimation and inference with contaminated data.","authors":"Peiliang Zhang, Wen-Xin Zhou, Zhao Ren","doi":"10.1214/26-EJS2492","DOIUrl":"10.1214/26-EJS2492","url":null,"abstract":"We study the problem of robust estimation and inference for contaminated data in distributed learning systems. Existing methods, such as those designed for Byzantine failures, typically assume that contamination is limited to a small subset of machines experiencing complete failure. In contrast, we address distributed partial contamination, where all machines may handle datasets containing some corrupted observations. By generalizing Huber's <math><mi>ϵ</mi></math> -contamination model to distributed settings, we propose a robust framework in which each machine's dataset may be contaminated by a proportion <math><mi>ϵ</mi></math> of observations from an arbitrary distribution. In a linear model setting, we develop a communication-efficient M-estimator that achieves the optimal convergence rate of centralized data. For robust inference, we introduce a distributed multiplier bootstrap method that requires no additional communication post-estimation while maintaining efficiency. To handle high contamination proportions, we present a debiasing procedure to mitigate bias. Extensive simulations demonstrate the robustness and scalability of our methods across diverse contamination scenarios.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":"20 1","pages":"261-323"},"PeriodicalIF":1.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13441429/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148680590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

High-dimensional partial linear model with trend filtering. 带有趋势滤波的高维部分线性模型。

IF 1.5 4区数学

Electronic Journal of Statistics Pub Date : 2026-01-01 Epub Date: 2026-05-13 DOI: 10.1214/26-ejs2522

Sang Kyu Lee, Erikka Loftfield, Hyokyoung G Hong, Haolei Weng

{"title":"High-dimensional partial linear model with trend filtering.","authors":"Sang Kyu Lee, Erikka Loftfield, Hyokyoung G Hong, Haolei Weng","doi":"10.1214/26-ejs2522","DOIUrl":"10.1214/26-ejs2522","url":null,"abstract":"Understanding the links between diet, metabolic changes, and health outcomes is a key focus in nutritional science and broader biological research. Analyzing relationships, such as those between ultra-processed food (UPF) intake and metabolites, offers insights into potential biomarkers for diet-related diseases and public health applications. However, these analyses are challenging due to high-dimensional data structures and complex, often nonlinear associations between covariates and health outcomes. Traditional linear models and conventional nonparametric methods often lack the flexibility to accurately capture such complexities in biological data. To address these challenges, we propose a high-dimensional partial linear regression model that captures both linear and nonlinear effects, combining the interpretability of linear models with the adaptability of nonparametric approaches. Our model leverages trend filtering to handle local smoothness variations effectively and achieves minimax optimal rates, making it suitable for complex biological datasets. We apply this model to data from the Interactive Diet and Activity Tracking in AARP (IDATA) Study, demonstrating its utility in identifying biomarkers associated with UPF intake and illustrating its potential for broader applications in dietary, metabolic, and health-related research.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":"20 1","pages":"1800-1850"},"PeriodicalIF":1.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13568697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148957279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Regression analysis of semiparametric Cox-Aalen transformation models with partly interval-censored data. 采用部分区间删失数据的半参数 Cox-Aalen 转换模型的回归分析。

IF 1.3 4区数学

Electronic Journal of Statistics Pub Date : 2025-01-01 Epub Date: 2025-01-13 DOI: 10.1214/24-ejs2341

Xi Ninga, Yanqing Sun, Yinghao Pan, Peter B Gilbert

{"title":"Regression analysis of semiparametric Cox-Aalen transformation models with partly interval-censored data.","authors":"Xi Ninga, Yanqing Sun, Yinghao Pan, Peter B Gilbert","doi":"10.1214/24-ejs2341","DOIUrl":"10.1214/24-ejs2341","url":null,"abstract":"Partly interval-censored data, comprising exact and intervalcensored observations, are prevalent in biomedical, clinical, and epidemiological studies. This paper studies a flexible class of the semiparametric Cox-Aalen transformation models for regression analysis of such data. These models offer a versatile framework by accommodating both multiplicative and additive covariate effects and both constant and time-varying effects within a transformation, while also allowing for potentially time-dependent covariates. Moreover, this class of models includes many popular models such as the semiparametric transformation model, the Cox-Aalen model, the stratified Cox model, and the stratified proportional odds model as special cases. To facilitate efficient computation, we formulate a set of estimating equations and propose an Expectation-Solving (ES) algorithm that guarantees stability and rapid convergence. Under mild regularity assumptions, the resulting estimator is shown to be consistent and asymptotically normal. The validity of the weighted bootstrap is also established. A supremum test is proposed to test the time-varying covariate effects. Finally, the proposed method is evaluated through comprehensive simulations and applied to analyze data from a randomized HIV/AIDS trial.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":"19 1","pages":"240-290"},"PeriodicalIF":1.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11828658/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Selective Inference for Sparse Graphs via Neighborhood Selection. 基于邻域选择的稀疏图选择推理。

IF 1.5 4区数学

Electronic Journal of Statistics Pub Date : 2025-01-01 Epub Date: 2025-09-05 DOI: 10.1214/25-ejs2429

Yiling Huang, Snigdha Panigrahi, Walter Dempsey

{"title":"Selective Inference for Sparse Graphs via Neighborhood Selection.","authors":"Yiling Huang, Snigdha Panigrahi, Walter Dempsey","doi":"10.1214/25-ejs2429","DOIUrl":"10.1214/25-ejs2429","url":null,"abstract":"Neighborhood selection is a widely used method used for estimating the support set of sparse precision matrices, which helps determine the conditional dependence structure in undirected graphical models. However, reporting only point estimates for the estimated graph can result in poor replicability without accompanying uncertainty estimates. In fields such as psychology, where the lack of replicability is a major concern, there is a growing need for methods that can address this issue. In this paper, we focus on the Gaussian graphical model. We introduce a selective inference method to attach uncertainty estimates to the selected (nonzero) entries of the precision matrix and decide which of the estimated edges must be included in the graph. Our method provides an exact adjustment for the selection of edges, which when multiplied with the Wishart density of the random matrix, results in valid selective inferences. Through the use of externally added randomization variables, our adjustment is easy to compute, requiring us to calculate the probability of a selection event, that is equivalent to a few sign constraints and that decouples across the nodewise regressions. Through simulations and an application to a mobile health trial designed to study mental health, we demonstrate that our selective inference method results in higher power and improved estimation accuracy.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":"19 2","pages":"4083-4116"},"PeriodicalIF":1.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12631974/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145589764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Asymmetric canonical correlation analysis of Riemannian and high-dimensional data. 黎曼和高维数据的非对称典型相关分析。

IF 1.5 4区数学

Electronic Journal of Statistics Pub Date : 2025-01-01 Epub Date: 2025-12-18 DOI: 10.1214/25-ejs2468

James Buenfil, Eardi Lila

{"title":"Asymmetric canonical correlation analysis of Riemannian and high-dimensional data.","authors":"James Buenfil, Eardi Lila","doi":"10.1214/25-ejs2468","DOIUrl":"10.1214/25-ejs2468","url":null,"abstract":"In this paper, we introduce a novel statistical model for the integrative analysis of Riemannian-valued functional data and high-dimensional data. We apply this model to explore the dependence structure between each subject's dynamic functional connectivity - represented by a temporally indexed collection of positive definite covariance matrices - and high-dimensional data representing lifestyle, demographic, and psychometric measures. Specifically, we employ a reformulation of canonical correlation analysis that enables efficient control of the complexity of the functional canonical directions using tangent space sieve approximations. Additionally, we enforce an interpretable group structure on the high-dimensional canonical directions via a sparsity-promoting penalty. The proposed method shows improved empirical performance over alternative approaches and comes with theoretical guarantees. Its application to data from the Human Connectome Project reveals a dominant mode of covariation between dynamic functional connectivity and lifestyle, demographic, and psychometric measures. This mode aligns with results from static connectivity studies but reveals a unique temporal non-stationary pattern that such studies fail to capture.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":"19 2","pages":"6077-6102"},"PeriodicalIF":1.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13020753/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147576520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Direct Bayesian linear regression for distribution-valued covariates. 分布值协变量的直接贝叶斯线性回归。

IF 1 4区数学

Electronic Journal of Statistics Pub Date : 2024-01-01 Epub Date: 2024-08-27 DOI: 10.1214/24-ejs2275

Bohao Tang, Sandipan Pramanik, Yi Zhao, Brian Caffo, Abhirup Datta

{"title":"Direct Bayesian linear regression for distribution-valued covariates.","authors":"Bohao Tang, Sandipan Pramanik, Yi Zhao, Brian Caffo, Abhirup Datta","doi":"10.1214/24-ejs2275","DOIUrl":"10.1214/24-ejs2275","url":null,"abstract":"In this manuscript, we study scalar-on-distribution regression; that is, instances where subject-specific distributions or densities are the covariates, related to a scalar outcome via a regression model. In practice, only repeated measures are observed from those covariate distributions and common approaches first use these to estimate subject-specific density functions, which are then used as covariates in standard scalar-on-function regression. We propose a simple and direct method for linear scalar-on-distribution regression that circumvents the intermediate step of estimating subject-specific covariate densities. We show that one can directly use the observed repeated measures as covariates and endow the regression function with a Gaussian process prior to obtain a closed form or conjugate Bayesian inference. Our method subsumes the standard Bayesian non-parametric regression using Gaussian processes as a special case, corresponding to covariates being Dirac-distributions. The model is also invariant to any transformation or ordering of the repeated measures. Theoretically, we show that, despite only using the observed repeated measures from the true density-valued covariates that generated the data, the method can achieve an optimal estimation error bound of the regression function. The theory extends beyond i.i.d. settings to accommodate certain forms of within-subject dependence among the repeated measures. To our knowledge, this is the first theoretical study on Bayesian regression using distribution-valued covariates. We propose numerous extensions including a scalable implementation using low-rank Gaussian processes and a generalization to non-linear scalar-on-distribution regression. Through simulation studies, we demonstrate that our method performs substantially better than approaches that require an intermediate density estimation step especially with a small number of repeated measures per subject. We apply our method to study association of age with activity counts.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":"18 2","pages":"3327-3375"},"PeriodicalIF":1.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11466299/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142401736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Robust improvement of efficiency using information on covariate distribution. 利用协变量分布信息的鲁棒性效率改进。

IF 1 4区数学

Electronic Journal of Statistics Pub Date : 2024-01-01 Epub Date: 2024-11-22 DOI: 10.1214/24-ejs2311

Lu Mao

{"title":"Robust improvement of efficiency using information on covariate distribution.","authors":"Lu Mao","doi":"10.1214/24-ejs2311","DOIUrl":"10.1214/24-ejs2311","url":null,"abstract":"The marginal inference of an outcome variable can be improved by closely related covariates with a structured distribution. This differs from standard covariate adjustment in randomized trials, which exploits covariate-treatment independence rather than knowledge on the covariate distribution. Yet it can also be done robustly against misspecification of the outcome-covariate relationship. Starting with a standard estimating function involving only the outcome, we first use a working regression model to compute its conditional expectation given the covariates, and then remove the uninformative part under the covariate distribution model. This effectively projects the initial function onto the joint tangent space of the full data, thereby achieving local efficiency when the regression model is correct. Importantly, even with a faulty working model, the estimator remains unbiased as the subtracted term is always asymptotically centered. Further improvement is possible if the outcome distribution also has its own structure. To demonstrate the process, we consider three examples: one with fully parametric covariates, one with a covariate following a partial parametric model against others, and another with mutually independent covariates.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":"18 2","pages":"4640-4666"},"PeriodicalIF":1.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11633646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Reproducible parameter inference using bagged posteriors. 使用袋装后验的可重复参数推理。

IF 1.5 4区数学

Electronic Journal of Statistics Pub Date : 2024-01-01 Epub Date: 2024-03-28 DOI: 10.1214/24-ejs2237

Jonathan H Huggins, Jeffrey W Miller

{"title":"Reproducible parameter inference using bagged posteriors.","authors":"Jonathan H Huggins, Jeffrey W Miller","doi":"10.1214/24-ejs2237","DOIUrl":"10.1214/24-ejs2237","url":null,"abstract":"Under model misspecification, it is known that Bayesian posteriors often do not properly quantify uncertainty about true or pseudo-true parameters. Even more fundamentally, misspecification leads to a lack of reproducibility in the sense that the same model will yield contradictory posteriors on independent data sets from the true distribution. To define a criterion for reproducible uncertainty quantification under misspecification, we consider the probability that two credible sets constructed from independent data sets have nonempty overlap, and we establish a lower bound on this overlap probability that holds whenever the credible sets are valid confidence sets. We prove that credible sets from the standard posterior can strongly violate this bound, indicating that it is not internally coherent under misspecification. To improve reproducibility in an easy-to-use and widely applicable way, we propose to apply bagging to the Bayesian posterior (\"BayesBag\"); that is, to use the average of posterior distributions conditioned on bootstrapped datasets. We motivate BayesBag from first principles based on Jeffrey conditionalization and show that the bagged posterior typically satisfies the overlap lower bound. Further, we prove a Bernstein-Von Mises theorem for the bagged posterior, establishing its asymptotic normal distribution. We demonstrate the benefits of BayesBag via simulation experiments and an application to crime rate prediction.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":"18 1","pages":"1549-1585"},"PeriodicalIF":1.5,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12588188/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145460251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Should we estimate a product of density functions by a product of estimators? 我们应该用估计量的乘积来估计密度函数的乘积吗？

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2103

F. Comte, C. Duval

引用次数: 0

Statistical inference via conditional Bayesian posteriors in high-dimensional linear regression 基于条件贝叶斯后验的高维线性回归统计推断

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2113

Teng Wu, Naveen N. Narisetty, Yun Yang

引用次数: 2