.png){kind=icon}
Return
Integrating Multisource Block-Wise Missing Data in Model Selection
.png){kind=icon}
OA
AI
DOI：10.1080/01621459.2020.1751176.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
For multisource data, blocks of variable information from certain sources are likely missing. Existing methods for handling missing data do not take structures of block-wise missing data into consideration. In this article, we propose a multiple block-wise imputation (MBI) approach, which incorporates imputations based on both complete and incomplete observations. Specifically, for a given missing pattern group, the imputations in MBI incorporate more samples from groups with fewer observed variables in addition to the group with complete observations. We propose to construct estimating equations based on all available information, and integrate informative estimating functions to achieve efficient estimators. We show that the proposed method has estimation and model selection consistency under both fixed-dimensional and high-dimensional settings. Moreover, the proposed estimator is asymptotically more efficient than the estimator based on a single imputation from complete observations only. In addition, the proposed method is not restricted to missing completely at random. Numerical studies and ADNI data application confirm that the proposed method outperforms existing variable selection methods under various missing mechanisms. for this article are available online.
Keywords:
ADNI
Data integration
Dimension reduction
Generalized method of moments
Informative missing
Missing at random
Journal
J
IF:
3
Papers:
5.2K
Citations:
4.8W
Organization
Cited Papers
ShareSaveUsing resonance Raman cross‐section data to estimate the spin‐state populations of cytochromes P450
OAAI
OAAIShareSaveOn the right side? A longitudinal study of left- versus right-lateralized semantic dementia.png)
BRAINOAAI
BRAINIF11.7
OAAIShareSaveShareSaveRevealing the Structure Formation on Polyglycerol Citrate Polymers—An Environmentally Friendly Polyester as a Seed-Coating MaterialPolymersOAAI
PolymersIF0
OAAIShareSave
View more