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On Functional Sure Independence Screening (fSIS) and Dynamic Spectral Featuring Extraction for Neuroscience Data


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dc.contributor.advisorBillor, Nedret
dc.contributor.authorYuan, Yuan
dc.date.accessioned2021-12-07T21:51:38Z
dc.date.available2021-12-07T21:51:38Z
dc.date.issued2021-12-07
dc.identifier.urihttps://etd.auburn.edu//handle/10415/8064
dc.description.abstractThis dissertation is composed of two parts. The first part introduces a methodology for high-dimensional functional variable screening and selection--the functional sure independent screening (fSIS). The fSIS method is an extension of the Sure Independent Screening (SIS) and grouped Sure Independent Screening (gSIS) to functional data analysis. Extensive simulation studies and analysis of a real data application show that our proposed approach outperforms other approaches. The second part presents a novel dynamic spectral featuring extraction method for neuroscience data---the EnergySpectrum. This feature extracting approach is based on the time-frequency analysis of neuroscience data. In a Electroencephalography (EEG) data classification problem, the EnergySpecturm-based features are compared with power-spectral-density-based features as well as Riemaniann geometry-based features to classify EEG epochs for each participant into its lab condition. With cross-validation studies, we show that the EnergySpecturm features achieve better or equivalent classification accuracy compared with other features. Further, we also include a functional connectivity analysis study using Zebrafish Calcium imaging data. The study shows that our methods work well with clustering questions.en_US
dc.rightsEMBARGO_NOT_AUBURNen_US
dc.subjectMathematics and Statisticsen_US
dc.titleOn Functional Sure Independence Screening (fSIS) and Dynamic Spectral Featuring Extraction for Neuroscience Dataen_US
dc.typePhD Dissertationen_US
dc.embargo.lengthMONTHS_WITHHELD:60en_US
dc.embargo.statusEMBARGOEDen_US
dc.embargo.enddate2026-12-07en_US

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