Optimal Sparse Linear Prediction for Block-missing Multi-modality Data without Imputation.

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 practice, due to dropouts or the high cost of measures, the observations of a certain modality can be missing completely for some subjects. 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 predic

SUBMITTER: Yu G 

PROVIDER: S-EPMC8612700 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature