Campus Units
Statistics
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
2011
Journal or Book Title
Journal of the American Statistical Association
Volume
106
Issue
493
First Page
157
Last Page
165
DOI
10.1198/jasa.2011.tm10104
Abstract
Parameter estimation with nonignorable missing data is a challenging problem in statistics. The fully parametric approach for joint modeling of the response model and the population model can produce results that are quite sensitive to the failure of the assumed model. We propose a more robust modeling approach by considering the model for the nonresponding part as an exponential tilting of the model for the responding part. The exponential tilting model can be justified under the assumption that the response probability can be expressed as a semiparametric logistic regression model.
In this paper, based on the exponential tilting model, we propose a semiparametric estimation method of mean functionals with nonignorable missing data. A semiparametric logistic regression model is assumed for the response probability and a nonparametric regression approach for missing data discussed in Cheng (1994) is used in the estimator. By adopting nonparametric components for the model, the estimation method can be made robust. Variance estimation is also discussed and results from a simulation study are presented. The proposed method is applied to real income data from the Korean Labor and Income Panel Survey.
Copyright Owner
American Statistical Association
Copyright Date
2011
Language
en
File Format
application/pdf
Recommended Citation
Kim, Jae Kwang and Yu, Cindy Long, "A Semiparametric Estimation of Mean Functionals With Nonignorable Missing Data" (2011). Statistics Publications. 103.
https://lib.dr.iastate.edu/stat_las_pubs/103
Included in
Design of Experiments and Sample Surveys Commons, Multivariate Analysis Commons, Statistical Methodology Commons
Comments
This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association in 2011; available online: http://dx.doi.org/10.1198/jasa.2011.tm10104. DOI:10.1198/jasa.2011.tm10104. Posted with permission.