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dc.contributor.authorGauran, Iris Ivy Mirales
dc.contributor.authorXue, Gui
dc.contributor.authorChen, Chuansheng
dc.contributor.authorOmbao, Hernando
dc.contributor.authorYu, Zhaoxia
dc.date.accessioned2022-04-12T08:26:15Z
dc.date.available2022-04-12T08:26:15Z
dc.date.issued2022-03-24
dc.identifier.citationGauran, I. I., Xue, G., Chen, C., Ombao, H., & Yu, Z. (2022). Ridge Penalization in High-Dimensional Testing With Applications to Imaging Genetics. Frontiers in Neuroscience, 16. https://doi.org/10.3389/fnins.2022.836100
dc.identifier.issn1662-4548
dc.identifier.pmid35401090
dc.identifier.doi10.3389/fnins.2022.836100
dc.identifier.urihttp://hdl.handle.net/10754/676227
dc.description.abstractHigh-dimensionality is ubiquitous in various scientific fields such as imaging genetics, where a deluge of functional and structural data on brain-relevant genetic polymorphisms are investigated. It is crucial to identify which genetic variations are consequential in identifying neurological features of brain connectivity compared to merely random noise. Statistical inference in high-dimensional settings poses multiple challenges involving analytical and computational complexity. A widely implemented strategy in addressing inference goals is penalized inference. In particular, the role of the ridge penalty in high-dimensional prediction and estimation has been actively studied in the past several years. This study focuses on ridge-penalized tests in high-dimensional hypothesis testing problems by proposing and examining a class of methods for choosing the optimal ridge penalty. We present our findings on strategies to improve the statistical power of ridge-penalized tests and what determines the optimal ridge penalty for hypothesis testing. The application of our work to an imaging genetics study and biological research will be presented.
dc.publisherFrontiers Media SA
dc.relation.urlhttps://www.frontiersin.org/articles/10.3389/fnins.2022.836100/full
dc.rightsArchived with thanks to Frontiers in neuroscience under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0/
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectGenome-wide Association Studies
dc.subjectNeuroimaging
dc.subjectImaging Genetics
dc.subjectHigh-dimensional Testing
dc.subjectRidge Penalization
dc.titleRidge Penalization in High-Dimensional Testing With Applications to Imaging Genetics
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalFrontiers in neuroscience
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionCenter for Brain and Learning Science, Beijing Normal University, Beijing, China
dc.contributor.institutionDepartment of Psychological Science, University of California, Irvine, Irvine, CA, United States
dc.contributor.institutionDepartment of Statistics, University of California, Irvine, Irvine, CA, United States
dc.identifier.volume16
kaust.personGauran, Iris Ivy Mirales
kaust.personOmbao, Hernando
refterms.dateFOA2022-04-12T08:27:31Z


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Archived with thanks to Frontiers in neuroscience under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0/
Except where otherwise noted, this item's license is described as Archived with thanks to Frontiers in neuroscience under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0/