Multiple Imputation: A Review of Practical and Theoretical Findings

被引：127

作者：

Murray, Jared S. ^{[1
,2
]}

机构：

[1] Univ Texas Austin, Dept Informat Risk & Operat Management, Stat, 12110 Speedway B6500, Austin, TX 78712 USA

[2] Univ Texas Austin, Dept Stat & Data Sci, 12110 Speedway B6500, Austin, TX 78712 USA

来源：

STATISTICAL SCIENCE | 2018年 / 33卷 / 02期

基金：

美国国家科学基金会;

关键词：

Missing data; proper imputation; congeniality; chained equations; fully conditional specification; sequential regression multivariate imputation; GENERALIZED LINEAR-MODELS; BAYESIAN MIXTURE-MODELS; MISSING-DATA; CATEGORICAL-DATA; MEASUREMENT-ERROR; DATA SETS; VALUES; DIAGNOSTICS; MICE; BIAS;

D O I：

10.1214/18-STS644

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Multiple imputation is a straightforward method for handling missing data in a principled fashion. This paper presents an overview of multiple imputation, including important theoretical results and their practical implications for generating and using multiple imputations. A review of strategies for generating imputations follows, including recent developments in flexible joint modeling and sequential regression/chained equations/fully conditional specification approaches. Finally, we compare and contrast different methods for generating imputations on a range of criteria before identifying promising avenues for future research.

引用

页码：142 / 159

页数：18

共 105 条

[21] A comparison of inclusive and restrictive strategies in modern missing data procedures [J].