Integrating Multisource Block-Wise Missing Data in Model Selection

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

zbMATH Keywords

dimension reductiongeneralized method of momentsdata integrationmissing at randomADNIinformative missing

Mathematics Subject Classification ID

Missing data (62D10)

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

• glmnet

Cites Work

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