Journal of the American Statistical Association最新文献

{"title":"Variational Nonparametric Inference in Stochastic Block Models with Functional Covariates","authors":"Zuofeng Shang, Peijun Sang, Yang Feng, Chong Jin","doi":"10.1080/01621459.2026.2654876","DOIUrl":"https://doi.org/10.1080/01621459.2026.2654876","url":null,"abstract":"","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"71 1","pages":"1-13"},"PeriodicalIF":3.7,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online Change Rate Learning for Functional Data","authors":"Deru Kong, Ping Yu, Tiejun Tong, WenWu Wang","doi":"10.1080/01621459.2026.2687693","DOIUrl":"https://doi.org/10.1080/01621459.2026.2687693","url":null,"abstract":"","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"1 1","pages":"1-28"},"PeriodicalIF":3.7,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deconfounding via Profiled Transfer Learning","authors":"Ziyuan Chen, Yifan Jiang, Jingyuan Liu, Fang Yao","doi":"10.1080/01621459.2026.2686441","DOIUrl":"https://doi.org/10.1080/01621459.2026.2686441","url":null,"abstract":"","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"332 1","pages":"1-28"},"PeriodicalIF":3.7,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Calibrated Model Criticism Using Split Predictive Checks.","authors":"Jiawei Li, Jonathan Hunter Huggins","doi":"10.1080/01621459.2026.2649585","DOIUrl":"10.1080/01621459.2026.2649585","url":null,"abstract":"<p><p>Assessing how well a Bayesian model generalizes to unobserved data is essential, yet existing general-purpose model checks are either not properly calibrated (as in posterior predictive checks) or fail to be sufficiently general for practical use (e.g., due to requiring model-specific derivations). We propose <i>split predictive checks (SPCs)</i> as a simple, general-purpose class of predictive checks that maintain the usability of posterior predictive checks while directly targeting predictive generalization. SPCs work by splitting the data into training and test subsets, then fitting the model to the former and evaluating predictive discrepancies on the latter. We develop an asymptotic theory for two variants-single SPCs and divided SPCs-and show that, unlike posterior predictive checks, both yield asymptotically calibrated (hence interpretable) <i>p</i>-values. Our results show that single SPCs work well at identifying substantial misspecification, while divided SPCs are sensitive even to subtle departures from modeling assumptions. Through simulation studies and real-data applications, we show that SPCs provide reliable, flexible, and computationally efficient assessments of Bayesian model fit, often revealing issues with predictive generalization missed by other predictive checks. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.</p>","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13441283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148679078","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}

{"title":"Optimal Network-Guided Covariate Selection for High-Dimensional Data Integration","authors":"Tao Shen, Wanjie Wang","doi":"10.1080/01621459.2026.2685341","DOIUrl":"https://doi.org/10.1080/01621459.2026.2685341","url":null,"abstract":"","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"49 1","pages":"1-27"},"PeriodicalIF":3.7,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian Transfer Learning for Enhanced Estimation and Inference","authors":"Daoyuan Lai, Oscar Hernan Madrid Padilla, Xiang Li, Tian Gu","doi":"10.1080/01621459.2026.2685359","DOIUrl":"https://doi.org/10.1080/01621459.2026.2685359","url":null,"abstract":"","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"30 1","pages":"1-28"},"PeriodicalIF":3.7,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of Public Health Policy by Digital Twin Microsimulation and Q-learning: A COVID-19 Booster Case Study","authors":"Guoxuan Ma, Sicong Xie, Lili Zhao, Jian Kang","doi":"10.1080/01621459.2026.2682540","DOIUrl":"https://doi.org/10.1080/01621459.2026.2682540","url":null,"abstract":"","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"34 1","pages":"1-18"},"PeriodicalIF":3.7,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learn Then Decide: A Learning Approach for Designing Data Marketplaces.","authors":"Yingqi Gao, Wenlu Xu, Jin J Zhou, Hua Zhou, Yong Chen, Xiaowu Dai","doi":"10.1080/01621459.2026.2655549","DOIUrl":"10.1080/01621459.2026.2655549","url":null,"abstract":"<p><p>As data marketplaces become increasingly central to the digital economy, it is crucial to design efficient pricing mechanisms that optimize revenue while ensuring fair and adaptive pricing. We introduce the Maximum Auction-to-Posted Price (MAPP) mechanism, a novel two-stage approach that first estimates the bidders' value distribution through auctions and then determines the optimal posted price based on the learned distribution. We establish that MAPP is individually rational and incentive-compatible, which ensures truthful bidding while balancing revenue maximization and minimizing price discrimination. On the theoretical side, we establish a statistical viewpoint that recasts revenue optimization as a valuation density estimation problem: we show that revenue regret can be controlled by uniform error in estimating the valuation density. MAPP achieves a regret of <math> <msub><mrow><mi>O</mi></mrow> <mrow><mi>p</mi></mrow> </msub> <mfenced> <mrow> <msup><mrow><mi>n</mi></mrow> <mrow><mo>-</mo> <mn>1</mn></mrow> </msup> <mo>(</mo> <mtext>log</mtext> <mspace></mspace> <mi>n</mi> <msup><mrow><mo>)</mo></mrow> <mrow><mn>2</mn></mrow> </msup> </mrow> </mfenced> </math> when incorporating historical bid data, where <math><mi>n</mi></math> is the number of bids in the current round. For sequential dataset sales over <math><mi>T</mi></math> rounds, we propose an online MAPP mechanism that dynamically adjusts pricing across datasets with varying value distributions. Our approach achieves no-regret learning, with the average regret converging at a rate of <math> <msub><mrow><mi>O</mi></mrow> <mrow><mi>p</mi></mrow> </msub> <mfenced> <mrow> <msup><mrow><mi>T</mi></mrow> <mrow><mo>-</mo> <mn>1</mn> <mo>/</mo> <mn>2</mn></mrow> </msup> <mo>(</mo> <mtext>log</mtext> <mspace></mspace> <mi>T</mi> <msup><mrow><mo>)</mo></mrow> <mrow><mn>2</mn></mrow> </msup> </mrow> </mfenced> </math> . We validate the effectiveness of MAPP through simulations and real-world data from the FCC AWS-3 spectrum auction. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.</p>","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13384441/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148604419","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}

{"title":"Modelling Spatial Density: Data, Methods, and R Applications in Statistics, Econometrics, and Machine Learning.","authors":"Ting Fung Ma","doi":"10.1080/01621459.2026.2685045","DOIUrl":"https://doi.org/10.1080/01621459.2026.2685045","url":null,"abstract":"","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"17 1","pages":"1-2"},"PeriodicalIF":3.7,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An AI-powered Bayesian Generative Modeling Approach for Causal Inference in Observational Studies.","authors":"Qiao Liu, Wing Hung Wong","doi":"10.1080/01621459.2026.2654227","DOIUrl":"10.1080/01621459.2026.2654227","url":null,"abstract":"<p><p>Causal inference in observational studies with high-dimensional covariates presents significant challenges. We introduce CausalBGM, an AI-powered Bayesian generative modeling approach that captures the causal relationship among covariates, treatment, and outcome. The core innovation is to estimate the individual treatment effect (ITE) by learning the individual-specific distribution of a low-dimensional latent feature set (e.g., latent confounders) that drives changes in both treatment and outcome. This individualized posterior representation yields estimates of the individual treatment effect (ITE) together with well-calibrated posterior intervals while mitigating confounding effect. CausalBGM is fitted through an iterative algorithm to update the model parameters and the latent features until convergence. This framework leverages the power of AI to capture complex dependencies among variables while adhering to the Bayesian principles. Extensive experiments demonstrate that CausalBGM achieves superior or competitive performance compared to state-of-the-art methods, particularly in scenarios with high-dimensional covariates and large-scale datasets. By addressing key limitations of existing methods, CausalBGM emerges as a robust and promising framework for advancing causal inference in a wide range of modern applications. The code for CausalBGM is available at https://github.com/liuq-lab/bayesgm. The document for CausalBGM is available at https://bayesgm.readthedocs.io. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.</p>","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13344347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148412027","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}