Parametric fractional imputation for missing data analysis

• 成果类型: Article
• 署名作者: Kim, Jae Kwang
• 署名单位: Iowa State University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asq073
• 发表日期: 2011
• 页码: 119132
• 关键词: incomplete data; inference; likelihood

摘要

Parametric fractional imputation is proposed as a general tool for missing data analysis. Using fractional weights, the observed likelihood can be approximated by the weighted mean of the imputed data likelihood. Computational efficiency can be achieved using the idea of importance sampling and calibration weighting. The proposed imputation method provides efficient parameter estimates for the model parameters specified in the imputation model and also provides reasonable estimates for parameters that are not part of the imputation model. Variance estimation is discussed and results from a limited simulation study are presented.