Imputed Factor Regression for High-dimensional Block-wise Missing Data

DOI10.5705/SS.202018.0008zbMath1439.62233OpenAlexW2956079250WikidataQ129793885 ScholiaQ129793885MaRDI QIDQ5109912

Annie Qu, Yan-Qing Zhang, Nian Sheng Tang

Publication date: 14 May 2020

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.5705/ss.202018.0008

zbMATH Keywords

dimension reductionfactor modelAlzheimer's diseaseprincipal componentdata imputationAlzheimer's disease neuroimaging initiativeblock-wise missing data

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to biology and medical sciences; meta analysis (62P10) Image analysis in multivariate analysis (62H35) Missing data (62D10)

Related Items (5)

Model averaging for generalized linear models in fragmentary data prediction ⋮ Weighted multiple blockwise imputation method for high-dimensional regression with blockwise missing data ⋮ Mallows model averaging with effective model size in fragmentary data prediction ⋮ Multinomial logistic factor regression for multi-source functional block-wise missing data ⋮ Integrative Factor Regression and Its Inference for Multimodal Data Analysis

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