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Activity Number: 302 - Statistical Methods for Data Integration
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 11:50 AM
Sponsor: International Chinese Statistical Association
Abstract #310986
Title: Learning Predictive Models from Block-Missing Multi-Modality Data
Author(s): Guan Yu*
Keywords: Multi-modality data; Block-missing; Classification; Sparse Regression; Huber's M-estimate; Nearest Neighbor

In modern scientific research, data are often collected from multiple modalities. Since different modalities could provide complementary information, statistical prediction methods using multi-modality data could deliver better prediction performance than using single modality data. However, one special challenge for using multi-modality data is related to block-missing data. In this talk, I will introduce several supervised learning methods for block-missing multi-modality data, including the direct sparse regression procedure using covariance from multi-modality data for the regression problem and the weighted nearest neighbor classifier for the classification problem. The effectiveness of the proposed methods is demonstrated by theoretical studies, simulated examples, and a real application from the Alzheimer's Disease Neuroimaging Initiative. The comparison between our proposed methods and some existing methods also indicates the advantages of our methods.

Authors who are presenting talks have a * after their name.

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