Pattern graphs: a graphical approach to nonmonotone missing data

2020/04/01 by Yen‐Chi Chen, Chen, Yen-Chi · 2 citations
Computer Science · Mathematics · #65D18 #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #FOS: Mathematics #Methodology (stat.ME) #Primary 62F30 #Statistical Methods and Inference #Statistics Theory (math.ST) #secondary 62H05

paper · pdf · doi:10.48550/arxiv.2004.00744

openalex publication_date 2020/04/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We introduce the concept of pattern graphs--directed acyclic graphs representing how response patterns are associated. A pattern graph represents an identifying restriction that is nonparametrically identified/saturated and is often a missing not at random restriction. We introduce a selection model and a pattern mixture model formulations using the pattern graphs and show that they are equivalent. A pattern graph leads to an inverse probability weighting estimator as well as an imputation-based estimator. We also study the semi-parametric efficiency theory and derive a multiply-robust estimator using pattern graphs.

Cited by

• Markov Missing Graph: A Graphical Approach for Missing Data Imputation
• Recursive Equations For Imputation Of Missing Not At Random Data With Sparse Pattern Support

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