{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:36:13Z","timestamp":1765546573400,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030501525"},{"type":"electronic","value":"9783030501532"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-50153-2_30","type":"book-chapter","created":{"date-parts":[[2020,6,5]],"date-time":"2020-06-05T21:03:01Z","timestamp":1591390981000},"page":"391-404","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Covariate-Adjusted Hybrid Principal Components Analysis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3212-748X","authenticated-orcid":false,"given":"Aaron Wolfe","family":"Scheffler","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1936-2135","authenticated-orcid":false,"given":"Abigail","family":"Dickinson","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1927-8590","authenticated-orcid":false,"given":"Charlotte","family":"DiStefano","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2955-6902","authenticated-orcid":false,"given":"Shafali","family":"Jeste","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7619-5428","authenticated-orcid":false,"given":"Damla","family":"\u015eent\u00fcrk","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,6,5]]},"reference":[{"issue":"523","key":"30_CR1","doi-asserted-by":"publisher","first-page":"1003","DOI":"10.1080\/01621459.2017.1379403","volume":"113","author":"D Backenroth","year":"2018","unstructured":"Backenroth, D., Goldsmith, J., Harran, M.D., Cortes, J.C., Krakauer, J.W., Kitago, T.: Modeling motor learning using heteroscedastic functional principal components analysis. J. Am. Stat. Assoc. 113(523), 1003\u20131015 (2018)","journal-title":"J. Am. Stat. Assoc."},{"issue":"2","key":"30_CR2","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1111\/j.1467-9469.2006.00521.x","volume":"34","author":"H Cardot","year":"2007","unstructured":"Cardot, H.: Conditional functional principal components analysis. Scand. J. Stat. 34(2), 317\u2013335 (2007)","journal-title":"Scand. J. Stat."},{"issue":"C","key":"30_CR3","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.csda.2017.11.002","volume":"120","author":"J Cederbaum","year":"2018","unstructured":"Cederbaum, J., Scheipl, F., Greven, S.: Fast symmetric additive covariance smoothing. Comput. Stat. Data Anal. 120(C), 25\u201341 (2018)","journal-title":"Comput. Stat. Data Anal."},{"issue":"1","key":"30_CR4","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1111\/rssb.12160","volume":"79","author":"K Chen","year":"2016","unstructured":"Chen, K., Delicado, P., M\u00fcller, H.G.: Modelling function-valued stochastic processes, with applications to fertility dynamics. J. Roy. Stat. Soc.: Ser. B (Methodol.) 79(1), 177\u2013196 (2016)","journal-title":"J. Roy. Stat. Soc.: Ser. B (Methodol.)"},{"issue":"2","key":"30_CR5","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1111\/1467-9868.00393","volume":"65","author":"JM Chiou","year":"2003","unstructured":"Chiou, J.M., M\u00fcller, H.G., Wang, J.L.: Functional quasi-likelihood regression models with smooth random effects. J. Roy. Stat. Soc. Ser. B (Stat. Methodol.) 65(2), 405\u2013423 (2003)","journal-title":"J. Roy. Stat. Soc. Ser. B (Stat. Methodol.)"},{"issue":"7","key":"30_CR6","doi-asserted-by":"publisher","first-page":"e13064","DOI":"10.1111\/psyp.13064","volume":"55","author":"AW Corcoran","year":"2018","unstructured":"Corcoran, A.W., Alday, P.M., Schlesewsky, M., Bornkessel-Schlesewsky, I.: Toward a reliable, automated method of individual alpha frequency (IAF) quantification. Psychophysiology 55(7), e13064 (2018)","journal-title":"Psychophysiology"},{"issue":"6","key":"30_CR7","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1111\/ejn.13645","volume":"47","author":"A Dickinson","year":"2018","unstructured":"Dickinson, A., DiStefano, C., Senturk, D., Jeste, S.S.: Peak alpha frequency is a neural marker of cognitive function across the autism spectrum. Eur. J. Neurosci. 47(6), 643\u2013651 (2018)","journal-title":"Eur. J. Neurosci."},{"issue":"11","key":"30_CR8","doi-asserted-by":"publisher","first-page":"3288","DOI":"10.1002\/hbm.24598","volume":"40","author":"JC Edgar","year":"2019","unstructured":"Edgar, J.C., et al.: Abnormal maturation of the resting-state peak alpha frequency in children with autism spectrum disorder. Hum. Brain Mapp. 40(11), 3288\u20133298 (2019)","journal-title":"Hum. Brain Mapp."},{"issue":"3","key":"30_CR9","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1007\/s10803-014-2236-1","volume":"45","author":"JC Edgar","year":"2015","unstructured":"Edgar, J.C., et al.: Resting-state alpha in autism spectrum disorder and alpha associations with thalamic volume. J. Autism Dev. Disord. 45(3), 795\u2013804 (2015)","journal-title":"J. Autism Dev. Disord."},{"issue":"2","key":"30_CR10","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1214\/09-AOS742","volume":"38","author":"CR Jiang","year":"2010","unstructured":"Jiang, C.R., Wang, J.L.: Covariate adjusted functional principal components analysis for longitudinal data. Ann. Statist. 38(2), 1194\u20131226 (2010)","journal-title":"Ann. Statist."},{"issue":"4","key":"30_CR11","first-page":"815","volume":"105","author":"B Lynch","year":"2018","unstructured":"Lynch, B., Chen, K.: A test of weak separability for multi-way functional data, with application to brain connectivity studies. Biometrika 105(4), 815\u2013831 (2018)","journal-title":"Biometrika"},{"issue":"Supplement C","key":"30_CR12","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.neuroimage.2015.06.013","volume":"118","author":"V Miskovic","year":"2015","unstructured":"Miskovic, V., et al.: Developmental changes in spontaneous electrocortical activity and network organization from early to late childhood. NeuroImage 118(Supplement C), 237\u2013247 (2015)","journal-title":"NeuroImage"},{"issue":"2","key":"30_CR13","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/0013-4694(89)90180-6","volume":"72","author":"F Perrin","year":"1989","unstructured":"Perrin, F., Pernier, J., Bertrand, O., Echallier, J.: Spherical splines for scalp potential and current density mapping. Electroencephalogr. Clin. Neurophysiol. 72(2), 184\u2013187 (1989)","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"issue":"1","key":"30_CR14","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1093\/biostatistics\/kxy034","volume":"21","author":"AW Scheffler","year":"2018","unstructured":"Scheffler, A.W., et al.: Hybrid principal components analysis for region-referenced longitudinal functional EEG data. Biostatistics 21(1), 139\u2013157 (2018)","journal-title":"Biostatistics"},{"issue":"30","key":"30_CR15","doi-asserted-by":"publisher","first-page":"5587","DOI":"10.1002\/sim.8384","volume":"38","author":"AW Scheffler","year":"2019","unstructured":"Scheffler, A.W., et al.: Covariate-adjusted region-referenced generalized functional linear model for EEG data. Stat. Med. 38(30), 5587\u20135602 (2019)","journal-title":"Stat. Med."},{"issue":"3","key":"30_CR16","doi-asserted-by":"publisher","first-page":"2328","DOI":"10.1016\/j.neuroimage.2009.10.030","volume":"49","author":"P Valdas-Hernandez","year":"2010","unstructured":"Valdas-Hernandez, P., et al.: White matter architecture rather than cortical surface area correlates with the EEG alpha rhythm. NeuroImage 49(3), 2328\u20132339 (2010)","journal-title":"NeuroImage"},{"key":"30_CR17","doi-asserted-by":"publisher","DOI":"10.1201\/9781315370279","volume-title":"Generalized Additive Models: An Introduction with R","author":"S Wood","year":"2017","unstructured":"Wood, S.: Generalized Additive Models: An Introduction with R. Chapman and Hall\/CRC, London (2017)"}],"container-title":["Communications in Computer and Information Science","Information Processing and Management of Uncertainty in Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-50153-2_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,6,5]],"date-time":"2020-06-05T23:33:20Z","timestamp":1591400000000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-50153-2_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030501525","9783030501532"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-50153-2_30","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"5 June 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IPMU","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lisbon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 June 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 June 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ipmu2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ipmu2020.inesc-id.pt\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"213","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"146","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"69% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The IPMU 2020 was held virtually due to the coronavirus pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}