.png){kind=icon}
返回
Introduction to Double Robust Methods for Incomplete Data
.png){kind=icon}
OA
AI
DOI：10.1214/18-STS647.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Most methods for handling incomplete data can be broadly classified as inverse probability weighting (IPW) strategies or imputation strategies. The former model the occurrence of incomplete data; the latter, the distribution of the missing variables given observed variables in each missingness pattern. Imputation strategies are typically more efficient, but they can involve extrapolation, which is difficult to diagnose and can lead to large bias. Double robust (DR) methods combine the two approaches. They are typically more efficient than IPW and more robust to model misspecification than imputation. We give a formal introduction to DR estimation of the mean of a partially observed variable, before moving to more general incomplete-data scenarios. We review strategies to improve the performance of DR estimators under model misspecification, reveal connections between DR estimators for incomplete data and design-consistent estimators used in sample surveys, and explain the value of double robustness when using flexible data-adaptive methods for IPW or imputation.
Keyword:
Augmented inverse probability weighting
calibration estimators
data-adaptive methods
doubly robust
empirical likelihood
imputation
inverse probability weighting
missing data
semiparametric methods
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.4
论文数:
1.0K
被引数:
8.7K
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容