Semiparametric maximum likelihood inference for nonignorable nonresponse with callbacks
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 responseprobabili...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2018
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/soe_research/2217 https://ink.library.smu.edu.sg/context/soe_research/article/3216/viewcontent/semiparametric_maximum_likelihood_inference.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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