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- Title
IMPUTED FACTOR REGRESSION FOR HIGH-DIMENSIONAL BLOCK-WISE MISSING DATA.
- Authors
Yanqing Zhang; Niansheng Tang; Annie Qu
- Abstract
Block-wise missing data are becoming increasingly common in high-dimensional biomedical, social, psychological, and environmental studies. As a result, we need effcient dimension-reduction methods for extracting important in-formation for predictions under such data. Existing dimension-reduction methods and feature combinations are inefiective for handling block-wise missing data. We propose a factor-model imputation approach that targets block-wise missing data, and use an imputed factor regression for the dimension reduction and prediction. Specifically, we first perform screening to identify the important features. Then, we impute these features based on the factor model, and build a factor regression model to predict the response variable based on the imputed features. The pro-posed method utilizes the essential information from all observed data as a result of the factor structure of the model. Furthermore, the method remains effcient even when the proportion of block-wise missing is high. We show that the imputed factor regression model and its predictions are consistent under regularity conditions. We compare the proposed method with existing approaches using simulation studies, after which we apply it to data from the Alzheimer's Disease Neuroimaging Initiative. Our numerical results confirm that the proposed method outperforms existing competitive approaches.
- Subjects
MISSING data (Statistics); MULTIPLE imputation (Statistics); ALZHEIMER'S disease; REGRESSION analysis; FACTOR structure; ENVIRONMENTAL sciences
- Publication
Statistica Sinica, 2020, Vol 30, Issue 2, p631
- ISSN
1017-0405
- Publication type
Article
- DOI
10.5705/ss.202018.0008