.png){kind=icon}
返回
PATTERN GRAPHS: A GRAPHICAL APPROACH TO NONMONOTONE MISSING DATA
.png){kind=icon}
OA
AI
DOI：10.1214/21-AOS2094.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 semiparametric efficiency theory and derive a multiply-robust estimator using pattern graphs.
Keyword:
Missing data
nonignorable missingness
nomonotone missing
inverse probability weighting
pattern graphs
selection models
期刊
IF:
3.7
论文数:
2.8K
被引数:
2.9W
机构
引用论文
.png){kind=icon}
查看更多内容