Integrating Multisource Block-Wise Missing Data in Model Selection
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Publication:5881971
DOI10.1080/01621459.2020.1751176zbMath1506.62242arXiv1901.03797OpenAlexW3015593040MaRDI QIDQ5881971
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Publication date: 14 March 2023
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1901.03797
dimension reductiongeneralized method of momentsdata integrationmissing at randomADNIinformative missing
Related Items (9)
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 ⋮ Variable selection for high‐dimensional generalized linear model with block‐missing data ⋮ Multinomial logistic factor regression for multi-source functional block-wise missing data ⋮ Orthogonalized Kernel Debiased Machine Learning for Multimodal Data Analysis ⋮ Penalized estimating equations for generalized linear models with multiple imputation ⋮ Optimal Integrating Learning for Split Questionnaire Design Type Data ⋮ BlockMissingData
Uses Software
Cites Work
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