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

In modern scientific research, data are often collected from multiple modalities. Since different modalities could provide complementary information, statistical prediction methods using multimodality data could deliver better prediction performance than using single modality data. However, one spec...

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Veröffentlicht in:	Journal of the American Statistical Association Jg. 115; H. 531; S. 1406 - 1419
Hauptverfasser:	Yu, Guan, Li, Quefeng, Shen, Dinggang, Liu, Yufeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Alexandria Taylor & Francis 02.07.2020 Taylor & Francis Ltd
Schlagworte:	Alzheimer disease Alzheimer's disease Americans Analysis of covariance Block-missing Continuity (mathematics) Covariance matrix dropouts Dropping out Huber's M-estimate Imputing Lasso Linear prediction Mathematical analysis Matrix algebra Matrix methods Medical imaging methodology Missing data Multi-modality Multimodality Neuroimaging Prediction Predictions Regression analysis Sparse regression Statistical analysis Statistical methods Statistics variance covariance matrix
ISSN:	0162-1459, 1537-274X, 1537-274X
Online-Zugang:	Volltext
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