Optimal Sparse Linear Prediction for Block-Missing Multi-Modality Data Without Imputation

• Activity Number: 501 - Biometrics Student Paper Awards 1
• Type: Topic Contributed
• Date/Time: Wednesday, August 2, 2017 : 10:30 AM to 12:15 PM
• Sponsor: Biometrics Section
• Author(s): Guan Yu* and Quefeng Li and Dinggang Shen and Yufeng Liu
• Companies: State University of New York at Buffalo and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and University of North Carolina
• Keywords: Block-missing ; Multi-modality data ; Optimal linear prediction ; Lasso ; Huber's M-estimate ; Neuroimaging

Abstract

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 paper, we propose a new DIrect Sparse regression procedure using COvariance from Multi-modality data (DISCOM). Our proposed DISCOM method includes two steps to find the optimal linear prediction of a continuous response variable using block-missing multi-modality predictors without imputation. The number of samples that are effectively used by DISCOM is the minimum number of samples with available observations from two modalities, which can be much larger than the number of samples with complete observations for all modalities. The effectiveness of the proposed method is demonstrated by theoretical studies, simulated examples, and a real application from the Alzheimer's Disease Neuroimaging Initiative. The comparison between DISCOM and some existing methods also indicates the advantages of our method.