Multiple imputation in multivariate problems when the imputation and analysis models differ

被引：112
|
作者：
Schafer, JL
机构：
[1] Penn State Univ, Dept Stat, University Pk, PA 16802 USA
[2] Penn State Univ, Methodol Ctr, University Pk, PA 16802 USA
来源：
STATISTICA NEERLANDICA | 2003年 / 57卷 / 01期
关键词：
missing data; nonresponse;
D O I：
10.1111/1467-9574.00218
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：
Bayesian multiple imputation (MI) has become a highly useful paradigm for handling missing values in many settings. In this paper, I compare Bayesian MI with other methods - maximum likelihood, in particular-and point out some of its unique features. One key aspect of MI, the separation of the imputation phase from the analysis phase, can be advantageous in settings where the models underlying the two phases do not agree.
引用
收藏
页码：19 / 35
页数：17
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