Handling Missing Data by Maximum Likelihood 论文
2012引用 362
Statistical Methods and Bayesian InferenceSAS software applications and methodsBayesian Methods and Mixture Models
详细信息
- 发表日期
- 2012-01-01
- 发表年份
- 2012
关键词
Statistical Methods and Bayesian InferenceSAS software applications and methodsBayesian Methods and Mixture Models
摘要
Multiple imputation is rapidly becoming a popular method for handling missing data, especially with easy-to-use software like PROC MI. In this paper, however, I argue that maximum likelihood is usually better than multiple imputation for several important reasons. I then demonstrate how maximum likelihood for missing data can readily be implemented with the following SAS ® procedures: MI, MIXED, GLIMMIX, CALIS and QLIM.