{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T17:01:09Z","timestamp":1786122069551,"version":"build-2736575974"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["Z240004"],"award-info":[{"award-number":["Z240004"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72525001"],"award-info":[{"award-number":["72525001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12401360"],"award-info":[{"award-number":["12401360"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12531012"],"award-info":[{"award-number":["12531012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72495124"],"award-info":[{"award-number":["72495124"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["2024ZD0525400"],"award-info":[{"award-number":["2024ZD0525400"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["2024ZD0525401"],"award-info":[{"award-number":["2024ZD0525401"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003213","name":"Beijing Municipal Education Commission","doi-asserted-by":"publisher","award":["KM202410028021"],"award-info":[{"award-number":["KM202410028021"]}],"id":[{"id":"10.13039\/501100003213","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012492","name":"Beijing Municipal Outstanding Young Talents","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012492","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.patcog.2026.113749","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T16:29:22Z","timestamp":1780331362000},"page":"113749","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PB","title":["Model aggregation-assisted transfer learning framework for generalized semiparametric models"],"prefix":"10.1016","volume":"180","author":[{"given":"Xiaonan","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanwei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"10","key":"10.1016\/j.patcog.2026.113749_b1","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.patcog.2026.113749_b2","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.113749_b3","series-title":"International Conference on Machine Learning","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","author":"Long","year":"2015"},{"key":"10.1016\/j.patcog.2026.113749_b4","doi-asserted-by":"crossref","unstructured":"Z. Qiu, X. Wu, J. Gao, W. Fan, U-BERT: Pre-training user representations for improved recommendation, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, 2021, pp. 4320\u20134327.","DOI":"10.1609\/aaai.v35i5.16557"},{"issue":"1","key":"10.1016\/j.patcog.2026.113749_b5","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A comprehensive survey on transfer learning","volume":"109","author":"Zhuang","year":"2020","journal-title":"Proc. IEEE"},{"issue":"5","key":"10.1016\/j.patcog.2026.113749_b6","doi-asserted-by":"crossref","first-page":"2964","DOI":"10.1287\/mnsc.2020.3729","article-title":"Predicting with proxies: Transfer learning in high dimension","volume":"67","author":"Bastani","year":"2021","journal-title":"Manage. Sci."},{"issue":"1","key":"10.1016\/j.patcog.2026.113749_b7","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1111\/rssb.12479","article-title":"Transfer learning for high-dimensional linear regression: Prediction, estimation and minimax optimality","volume":"84","author":"Li","year":"2021","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"issue":"544","key":"10.1016\/j.patcog.2026.113749_b8","doi-asserted-by":"crossref","first-page":"2684","DOI":"10.1080\/01621459.2022.2071278","article-title":"Transfer learning under high-dimensional generalized linear models","volume":"118","author":"Tian","year":"2023","journal-title":"J. Amer. Statist. Assoc."},{"key":"10.1016\/j.patcog.2026.113749_b9","first-page":"86176","article-title":"Co-regularization enhances knowledge transfer in high dimensions","volume":"38","author":"Liu","year":"2026","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"10.1016\/j.patcog.2026.113749_b10","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1214\/009053607000000604","article-title":"Variable selection in semiparametric regression modeling","volume":"36","author":"Li","year":"2008","journal-title":"Ann. Statist."},{"issue":"4","key":"10.1016\/j.patcog.2026.113749_b11","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1111\/j.1468-0262.2007.00785.x","article-title":"Least squares model averaging","volume":"75","author":"Hansen","year":"2007","journal-title":"Econometrica"},{"issue":"1","key":"10.1016\/j.patcog.2026.113749_b12","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1080\/07350015.2023.2166515","article-title":"Prediction using many samples with models possibly containing partially shared parameters","volume":"42","author":"Zhang","year":"2024","journal-title":"J. Bus. Econom. Statist."},{"issue":"358","key":"10.1016\/j.patcog.2026.113749_b13","first-page":"1","article-title":"Optimal parameter-transfer learning by semiparametric model averaging","volume":"24","author":"Hu","year":"2023","journal-title":"J. Mach. Learn. Res."},{"issue":"516","key":"10.1016\/j.patcog.2026.113749_b14","doi-asserted-by":"crossref","first-page":"1775","DOI":"10.1080\/01621459.2015.1115762","article-title":"Optimal model averaging estimation for generalized linear models and generalized linear mixed-effects models","volume":"111","author":"Zhang","year":"2016","journal-title":"J. Amer. Statist. Assoc."},{"issue":"1","key":"10.1016\/j.patcog.2026.113749_b15","doi-asserted-by":"crossref","DOI":"10.1002\/sta4.520","article-title":"Model averaging estimation for generalized partially linear varying-coefficient models","volume":"12","author":"Zheng","year":"2023","journal-title":"Stat"},{"key":"10.1016\/j.patcog.2026.113749_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112525","article-title":"Transfer learning for high-dimensional linear regression via the elastic net","volume":"304","author":"Meng","year":"2024","journal-title":"Knowl.-Based Syst."},{"issue":"1","key":"10.1016\/j.patcog.2026.113749_b17","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1012739","article-title":"A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data","volume":"21","author":"Pan","year":"2025","journal-title":"PLoS Comput. Biol."},{"issue":"543","key":"10.1016\/j.patcog.2026.113749_b18","doi-asserted-by":"crossref","first-page":"2171","DOI":"10.1080\/01621459.2022.2044333","article-title":"Transfer learning in large-scale Gaussian graphical models with false discovery rate control","volume":"118","author":"Li","year":"2023","journal-title":"J. Amer. Statist. Assoc."},{"key":"10.1016\/j.patcog.2026.113749_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110964","article-title":"Joint estimation for multisource Gaussian graphical models based on transfer learning","volume":"158","author":"Zhang","year":"2025","journal-title":"Pattern Recognit."},{"issue":"21","key":"10.1016\/j.patcog.2026.113749_b20","doi-asserted-by":"crossref","first-page":"4112","DOI":"10.1002\/sim.9499","article-title":"Transfer learning in high-dimensional semiparametric graphical models with application to brain connectivity analysis","volume":"41","author":"He","year":"2022","journal-title":"Stat. Med."},{"key":"10.1016\/j.patcog.2026.113749_b21","article-title":"Recent developments on statistical transfer learning","author":"Zhu","year":"2025","journal-title":"Int. Stat. Rev."},{"issue":"1","key":"10.1016\/j.patcog.2026.113749_b22","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.jeconom.2011.06.019","article-title":"Jackknife model averaging","volume":"167","author":"Hansen","year":"2012","journal-title":"J. Econometrics"},{"issue":"1","key":"10.1016\/j.patcog.2026.113749_b23","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.jeconom.2022.04.007","article-title":"Model averaging prediction by K-fold cross-validation","volume":"235","author":"Zhang","year":"2023","journal-title":"J. Econometrics"},{"issue":"4","key":"10.1016\/j.patcog.2026.113749_b24","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1111\/biom.12904","article-title":"Varying-coefficient semiparametric model averaging prediction","volume":"74","author":"Li","year":"2018","journal-title":"Biometrics"},{"issue":"526","key":"10.1016\/j.patcog.2026.113749_b25","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1080\/01621459.2018.1456936","article-title":"A Mallows-type model averaging estimator for the varying-coefficient partially linear model","volume":"114","author":"Zhu","year":"2019","journal-title":"J. Amer. Statist. Assoc."},{"key":"10.1016\/j.patcog.2026.113749_b26","first-page":"1","article-title":"Exponentially weighted averaging of varying-coefficient partially linear models","author":"Cui","year":"2025","journal-title":"J. Nonparametr. Stat."},{"issue":"1","key":"10.1016\/j.patcog.2026.113749_b27","doi-asserted-by":"crossref","DOI":"10.1002\/sta4.582","article-title":"The art of transfer learning: An adaptive and robust pipeline","volume":"12","author":"Wang","year":"2023","journal-title":"Stat"},{"key":"10.1016\/j.patcog.2026.113749_b28","series-title":"Generalized Linear Models","author":"Mccullagh","year":"1989"},{"key":"10.1016\/j.patcog.2026.113749_b29","series-title":"A Practical Guide to Splines","author":"De Boor","year":"2001"},{"issue":"6","key":"10.1016\/j.patcog.2026.113749_b30","doi-asserted-by":"crossref","first-page":"2654","DOI":"10.1214\/17-AOS1538","article-title":"A weight-relaxed model averaging approach for high-dimensional generalized linear models","volume":"45","author":"Ando","year":"2017","journal-title":"Ann. Statist."},{"issue":"2\u20134","key":"10.1016\/j.patcog.2026.113749_b31","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1080\/07474938.2023.2292377","article-title":"Post-averaging inference for optimal model averaging estimator in generalized linear models","volume":"43","author":"Yu","year":"2024","journal-title":"Econometric Rev."},{"issue":"7","key":"10.1016\/j.patcog.2026.113749_b32","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1080\/07474938.2022.2047508","article-title":"Optimal model averaging for divergent-dimensional Poisson regressions","volume":"41","author":"Zou","year":"2022","journal-title":"Econometric Rev."},{"issue":"4","key":"10.1016\/j.patcog.2026.113749_b33","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1080\/10618600.2023.2174127","article-title":"Multifold cross-validation model averaging for generalized additive partial linear models","volume":"32","author":"Chen","year":"2023","journal-title":"J. Comput. Graph. Statist."},{"issue":"1","key":"10.1016\/j.patcog.2026.113749_b34","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1111\/rssb.12023","article-title":"Model selection principles in misspecified models","volume":"76","author":"Lv","year":"2014","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"issue":"2","key":"10.1016\/j.patcog.2026.113749_b35","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1214\/aos\/1176349548","article-title":"Additive regression and other nonparametric models","volume":"13","author":"Stone","year":"1985","journal-title":"Ann. Statist."},{"issue":"4","key":"10.1016\/j.patcog.2026.113749_b36","doi-asserted-by":"crossref","first-page":"1827","DOI":"10.1214\/11-AOS885","article-title":"Estimation and variable selection for generalized additive partial linear models","volume":"39","author":"Wang","year":"2011","journal-title":"Ann. Statist."},{"key":"10.1016\/j.patcog.2026.113749_b37","series-title":"Proceedings of the Annual Symposium on Computer Application in Medical Care","first-page":"261","article-title":"Using the ADAP learning algorithm to forecast the onset of diabetes mellitus","author":"Smith","year":"1988"},{"issue":"1","key":"10.1016\/j.patcog.2026.113749_b38","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.jeconom.2014.07.002","article-title":"Distribution theory of the least squares averaging estimator","volume":"186","author":"Liu","year":"2015","journal-title":"J. Econometrics"},{"key":"10.1016\/j.patcog.2026.113749_b39","first-page":"1","article-title":"An asymptotic theory for least squares model averaging with nested models","author":"Fang","year":"2022","journal-title":"Econometric Theory"},{"key":"10.1016\/j.patcog.2026.113749_b40","series-title":"International Conference on Machine Learning","first-page":"10434","article-title":"Provable meta-learning of linear representations","author":"Tripuraneni","year":"2021"},{"key":"10.1016\/j.patcog.2026.113749_b41","doi-asserted-by":"crossref","unstructured":"W. Dai, Q. Yang, G.-R. Xue, Y. Yu, Boosting for transfer learning, in: Proceedings of the 24th International Conference on Machine Learning, 2007, pp. 193\u2013200.","DOI":"10.1145\/1273496.1273521"},{"key":"10.1016\/j.patcog.2026.113749_b42","series-title":"2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","first-page":"1855","article-title":"Boosting for transfer learning with multiple sources","author":"Yao","year":"2010"}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326007144?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326007144?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T08:21:51Z","timestamp":1786090911000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326007144"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":42,"alternative-id":["S0031320326007144"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113749","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Model aggregation-assisted transfer learning framework for generalized semiparametric models","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113749","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"113749"}}