Parametric fractional imputation for missing data analysis

Thumbnail Image
Date
2011-01-01
Authors
Kim, Jae Kwang
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Authors
Person
Kim, Jae Kwang
Professor
Research Projects
Organizational Units
Organizational Unit
Statistics
As leaders in statistical research, collaboration, and education, the Department of Statistics at Iowa State University offers students an education like no other. We are committed to our mission of developing and applying statistical methods, and proud of our award-winning students and faculty.
Journal Issue
Is Version Of
Versions
Series
Department
Statistics
Abstract

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.

Comments

This is a pre-copyedited, author-produced PDF of an article submitted for publication in Biometrika. The version of record (Kim, Jae Kwang. "Parametric fractional imputation for missing data analysis." Biometrika 98, no. 1 (2011): 119-132) is available online at doi:10.1093/biomet/asq073. Posted with permission.

Description
Keywords
Citation
DOI
Copyright
Sat Jan 01 00:00:00 UTC 2011
Collections