Pub Date : 2026-01-07DOI: 10.1080/01621459.2025.2606384
Sze Ming Lee, Yunxiao Chen, Tony Sit
{"title":"A Latent Variable Approach to Learning High-dimensional Multivariate longitudinal Data","authors":"Sze Ming Lee, Yunxiao Chen, Tony Sit","doi":"10.1080/01621459.2025.2606384","DOIUrl":"https://doi.org/10.1080/01621459.2025.2606384","url":null,"abstract":"","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"9 46 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145920011","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}
Pub Date : 2026-01-05DOI: 10.1080/01621459.2025.2573523
John Kornak, Karl Young, Eric Friedman, Konstantinos Bakas
Bayesian image analysis has been instrumental for over 40 years in addressing challenges such as image noise reduction, de-blurring, feature enhancement, and object detection. Despite its success, modeling spatial dependencies inherent to these problems often results in significant computational challenges. This work introduces the Bayesian Image Analysis in Fourier Space (BIFS) framework, which redefines conventional Bayesian modeling for continuous-valued images by transforming the problem into the Fourier domain. This transformation reduces the original high-dimensional dependent estimation problem into multiple low-dimensional, independent subproblems in Fourier space. The BIFS approach thereby simplifies computation while enabling flexible model specification, efficient formulation of isotropic priors, adaptability to diverse prior expectations, and invariance to changes in image resolution. BIFS thus offers a powerful and computationally efficient framework for a wide range of imaging applications. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
{"title":"Bayesian Image Analysis in Fourier Space.","authors":"John Kornak, Karl Young, Eric Friedman, Konstantinos Bakas","doi":"10.1080/01621459.2025.2573523","DOIUrl":"10.1080/01621459.2025.2573523","url":null,"abstract":"<p><p>Bayesian image analysis has been instrumental for over 40 years in addressing challenges such as image noise reduction, de-blurring, feature enhancement, and object detection. Despite its success, modeling spatial dependencies inherent to these problems often results in significant computational challenges. This work introduces the Bayesian Image Analysis in Fourier Space (BIFS) framework, which redefines conventional Bayesian modeling for continuous-valued images by transforming the problem into the Fourier domain. This transformation reduces the original high-dimensional dependent estimation problem into multiple low-dimensional, independent subproblems in Fourier space. The BIFS approach thereby simplifies computation while enabling flexible model specification, efficient formulation of isotropic priors, adaptability to diverse prior expectations, and invariance to changes in image resolution. BIFS thus offers a powerful and computationally efficient framework for a wide range of imaging applications. 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":3.0,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12890183/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146165605","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}
Pub Date : 2026-01-05DOI: 10.1080/01621459.2025.2605106
Lu Mao
{"title":"Likelihood Methods in Survival Analysis: With R Examples","authors":"Lu Mao","doi":"10.1080/01621459.2025.2605106","DOIUrl":"https://doi.org/10.1080/01621459.2025.2605106","url":null,"abstract":"","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"12 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145920408","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}
Pub Date : 2026-01-05DOI: 10.1080/01621459.2025.2606127
Ryan Martin
{"title":"Possibilistic inferential models: a review","authors":"Ryan Martin","doi":"10.1080/01621459.2025.2606127","DOIUrl":"https://doi.org/10.1080/01621459.2025.2606127","url":null,"abstract":"","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"3 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145920012","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}
Pub Date : 2026-01-05DOI: 10.1080/01621459.2025.2603731
Elynn Chen, Xi Chen, Wenbo Jing
{"title":"Data-Driven Knowledge Transfer in Batch Q* Learning","authors":"Elynn Chen, Xi Chen, Wenbo Jing","doi":"10.1080/01621459.2025.2603731","DOIUrl":"https://doi.org/10.1080/01621459.2025.2603731","url":null,"abstract":"","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"269 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145920407","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}
Pub Date : 2026-01-05DOI: 10.1080/01621459.2025.2602832
Mehmet Caner, Qingliang Fan
{"title":"Portfolio Analysis in High Dimensions with Tracking Error and Weight Constraints","authors":"Mehmet Caner, Qingliang Fan","doi":"10.1080/01621459.2025.2602832","DOIUrl":"https://doi.org/10.1080/01621459.2025.2602832","url":null,"abstract":"","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"47 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145920409","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}
Pub Date : 2025-12-10DOI: 10.1080/01621459.2025.2596297
Katarzyna Reluga, Dehan Kong, Setareh Ranjbar, Nicola Salvati, Mark van der Laan
{"title":"The impact of job stability on monetary poverty in Italy: causal small area estimation","authors":"Katarzyna Reluga, Dehan Kong, Setareh Ranjbar, Nicola Salvati, Mark van der Laan","doi":"10.1080/01621459.2025.2596297","DOIUrl":"https://doi.org/10.1080/01621459.2025.2596297","url":null,"abstract":"","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"362 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145753096","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}
Pub Date : 2025-12-10DOI: 10.1080/01621459.2025.2595732
Felix Reinbott, Anja Janßen
{"title":"Principal Component Analysis for max-stable distributions","authors":"Felix Reinbott, Anja Janßen","doi":"10.1080/01621459.2025.2595732","DOIUrl":"https://doi.org/10.1080/01621459.2025.2595732","url":null,"abstract":"","PeriodicalId":17227,"journal":{"name":"Journal of the American Statistical Association","volume":"102 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145753094","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}
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