Publication Type
Journal Article
Version
submittedVersion
Publication Date
12-2018
Abstract
We model the nonresponse probabilities as logistic functions ofthe outcome variable and other covariates in the survey sampling study withcallback. The identification aspect of this callback model is investigated. Semiparametricmaximum likelihood estimators of the parameters in the responseprobabilities are proposed and studied. As a result, an efficient estimator ofthe mean of the outcome variable is constructed using the estimated responseprobabilities. Moreover, if a regression model for conditional mean of the outcomevariable given some covariate is available, then we can obtain an evenmore efficient estimate of the mean of the outcome variable by fitting the regressionmodel using an adjusted least squares method based on the estimatedunderlying distributions of the observed values. Simulation results show theproposed method is more efficient compared with some existing competitors.The method is applied to data from a survey of health spending in a populationof individuals aged 50-70 years, where non-response can may be related tohealth.
Keywords
Auxiliary information, Calibration estimation, Followup, Logistic regression, Nonignorable nonresponse, Paradata
Discipline
Econometrics | Economic Theory
Research Areas
Econometrics
Publication
Scandinavian Journal of Statistics
Volume
45
Issue
4
First Page
962
Last Page
984
ISSN
0303-6898
Identifier
10.1111/sjos.12330
Publisher
Wiley
Citation
GUAN, Zhong; LEUNG, Denis H. Y.; and QIN, Jing.
Semiparametric maximum likelihood inference for nonignorable nonresponse with callbacks. (2018). Scandinavian Journal of Statistics. 45, (4), 962-984.
Available at: https://ink.library.smu.edu.sg/soe_research/2217
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1111/sjos.12330