About Shuhao Jiao Shuhao Jiao Postdoctoral Research Fellow, Statistics Biostatistics Shuhao Jiao is a Postdoctoral Fellow of the KAUST Biostatistics Research Group. Before joining KAUST, Dr. Jiao received his Ph.D. in Statistics in 2019 from University of California, Davis. He is interested in statistical learning of complicated neuroimage data. He addresses these problems using novel statistical theory and methods in functional data analysis and machine learning. Education 2019: Ph.D., Department of Statistics, University of California, Davis. 2014: BSc (Mathematics & Statistics), Shandong University, Jinan, China. Research Interest Functional data analysis, Time series Events Presented Events Mar 29 - Apr 4, 2020 Classification of Functional Data by Detecting the Discrepancy of Second Moment Structure of Scaled functions Shuhao Jiao, Postdoctoral Research Fellow, Statistics Apr 2, 12:00 - 13:00 KAUST Functional data This talk presents a new classification method for functional data. We consider the case where different groups of functions have similar means so that it is difficult to classify them based on only the mean function. To overcome this limitation, we propose the second moment based functional classifier (SMFC). Here, we demonstrate that the new method is sensitive to divergence in the second moment structure and thus produces lower rate of misclassification compared to other competitor methods. Our method uses the Hilbert-Schmidt norm to measure the divergence of second moment structure. One important innovation of our classification procedure lies in the dimension reduction step. The method data-adaptively discovers the basis functions that best capture the discrepancy between the second moment structures of the groups, rather than uses the functional principal component of each individual group, and good performance can be achieved as unnecessary variability is removed so that the classification accuracy is improved. Consistency properties of the classification procedure and the relevant estimators are established. Simulation study and real data analysis on phoneme and rat brain activity trajectories empirically validate the superiority of the proposed method.
Classification of Functional Data by Detecting the Discrepancy of Second Moment Structure of Scaled functions Shuhao Jiao, Postdoctoral Research Fellow, Statistics Apr 2, 12:00 - 13:00 KAUST Functional data This talk presents a new classification method for functional data. We consider the case where different groups of functions have similar means so that it is difficult to classify them based on only the mean function. To overcome this limitation, we propose the second moment based functional classifier (SMFC). Here, we demonstrate that the new method is sensitive to divergence in the second moment structure and thus produces lower rate of misclassification compared to other competitor methods. Our method uses the Hilbert-Schmidt norm to measure the divergence of second moment structure. One important innovation of our classification procedure lies in the dimension reduction step. The method data-adaptively discovers the basis functions that best capture the discrepancy between the second moment structures of the groups, rather than uses the functional principal component of each individual group, and good performance can be achieved as unnecessary variability is removed so that the classification accuracy is improved. Consistency properties of the classification procedure and the relevant estimators are established. Simulation study and real data analysis on phoneme and rat brain activity trajectories empirically validate the superiority of the proposed method.
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