Bayesian latent factor on image regression with nonignorable missing data

2020 ◽  
Author(s):  
Xiaoqing Wang ◽  
Xinyuan Song ◽  
Hongtu Zhu
Keyword(s):  
Missing Data ◽  
Nonignorable Missing Data ◽  
Latent Factor ◽  
Nonignorable Missing

Multiple Imputation Analysis for Nonignorable Missing Data

2021 ◽  
pp. 375-406
Author(s):  
Yulei He ◽  
Guangyu Zhang ◽  
Chiu-Hsieh Hsu
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Nonignorable Missing Data ◽  
Nonignorable Missing

scholarly journals Nonstandard conditionally specified models for nonignorable missing data

2020 ◽  
Vol 117 (32) ◽  
pp. 19045-19053
Author(s):  
Alexander M. Franks ◽  
Edoardo M. Airoldi ◽  
Donald B. Rubin
Keyword(s):  
Systems Biology ◽  
Missing Data ◽  
Joint Distribution ◽  
Exponential Family ◽  
Nonignorable Missing Data ◽  
Data Analyses ◽  
Missingness Mechanism ◽  
Conditionally Specified Models ◽  
Nonignorable Missing ◽  
Family Models

Data analyses typically rely upon assumptions about the missingness mechanisms that lead to observed versus missing data, assumptions that are typically unassessable. We explore an approach where the joint distribution of observed data and missing data are specified in a nonstandard way. In this formulation, which traces back to a representation of the joint distribution of the data and missingness mechanism, apparently first proposed by J. W. Tukey, the modeling assumptions about the distributions are either assessable or are designed to allow relatively easy incorporation of substantive knowledge about the problem at hand, thereby offering a possibly realistic portrayal of the data, both observed and missing. We develop Tukey’s representation for exponential-family models, propose a computationally tractable approach to inference in this class of models, and offer some general theoretical comments. We then illustrate the utility of this approach with an example in systems biology.

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