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.

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.

Copyright Owner

American Statistical Association

Language

en

File Format

application/pdf