{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T14:42:39Z","timestamp":1787668959816,"version":"build-2736575974"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,29]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>This paper develops a functional hybrid factor regression modelling framework to handle the heterogeneity of many large-scale imaging studies, such as the Alzheimer\u2019s disease neuroimaging initiative study. Despite the numerous successes of those imaging studies, such heterogeneity may be caused by the differences in study environment, population, design, protocols or other hidden factors, and it has posed major challenges in integrative analysis of imaging data collected from multicentres or multistudies. We propose both estimation and inference procedures for estimating unknown parameters and detecting unknown factors under our new model. The asymptotic properties of both estimation and inference procedures are systematically investigated. The finite-sample performance of our proposed procedures is assessed by using Monte Carlo simulations and a real data example on hippocampal surface data from the Alzheimer\u2019s disease study.<\/jats:p>","DOI":"10.1093\/biomet\/asac007","type":"journal-article","created":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T20:16:23Z","timestamp":1643228183000},"page":"1133-1148","source":"Crossref","is-referenced-by-count":12,"title":["Functional hybrid factor regression model for handling heterogeneity in imaging studies"],"prefix":"10.1093","volume":"109","author":[{"given":"C","family":"Huang","sequence":"first","affiliation":[{"name":"Florida State University Department of Statistics, , 117 N. Woodward Ave., Tallahassee, Florida 32304, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H","family":"Zhu","sequence":"additional","affiliation":[{"name":"The University of North Carolina at Chapel Hill Department of Biostatistics, , 135 Dauer Drive, Chapel Hill, North Carolina 27599, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,2,1]]},"reference":[{"key":"2022112917595538900_B1","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/j.neuroimage.2013.06.053","article-title":"Relationships between years of education and gray matter volume, metabolism and functional connectivity in healthy elders","volume":"83","author":"Arenaza-Urquijo,","year":"2013","journal-title":"NeuroImage"},{"key":"2022112917595538900_B2","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1214\/aos\/1013699998","article-title":"The control of the false discovery rate in multiple testing under dependency","volume":"29","author":"Benjamini,","year":"2001","journal-title":"Ann. 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