5 papers
Emputation: Identification-Guided Neural Imputation Framework
Yanjiao Yang, Yikun Zhang, Xinwei Shen +1
We propose Emputation, a deep generative framework for learning imputation models. Emputation targets the extrapolation distribution of missing variables given observed variables,…
Modeling Multivariate Missingness with Tree Graphs and Conjugate Odds
Daniel Suen, Yen-Chi Chen
In this paper, we analyze a specific class of missing not at random (MNAR) assumptions called tree graphs, extending upon the work of pattern graphs. We build off previous work by…
Modeling Missing at Random Neuropsychological Test Scores Using a Mixture of Binomial Product Experts
Daniel Suen, Yen-Chi Chen
Multivariate bounded discrete data arises in many fields. In the setting of dementia studies, such data is collected when individuals complete neuropsychological tests. We outline…
Masking criteria for selecting an imputation model
Yanjiao Yang, Daniel Suen, Yen-Chi Chen
The masking-one-out (MOO) procedure, masking an observed entry and comparing it versus its imputed values, is a very common procedure for comparing imputation models. We study the…
Markov Missing Graph: A Graphical Approach for Missing Data Imputation
Yanjiao Yang, Yen-Chi Chen
We introduce the Markov missing graph (MMG), a novel framework that imputes missing data based on undirected graphs. MMG leverages conditional independence relationships to locally…