Response and nonresponse

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  • 1. Nonparametric propensity weighting for survey nonresponse through local polynomial regression Archived
    Articles and reports: 12-001-X200900211039
    Description:

    Propensity weighting is a procedure to adjust for unit nonresponse in surveys. A form of implementing this procedure consists of dividing the sampling weights by estimates of the probabilities that the sampled units respond to the survey. Typically, these estimates are obtained by fitting parametric models, such as logistic regression. The resulting adjusted estimators may become biased when the specified parametric models are incorrect. To avoid misspecifying such a model, we consider nonparametric estimation of the response probabilities by local polynomial regression. We study the asymptotic properties of the resulting estimator under quasi-randomization. The practical behavior of the proposed nonresponse adjustment approach is evaluated on NHANES data.

    Release date: 2009-12-23
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  • 1. Nonparametric propensity weighting for survey nonresponse through local polynomial regression Archived
    Articles and reports: 12-001-X200900211039
    Description:

    Propensity weighting is a procedure to adjust for unit nonresponse in surveys. A form of implementing this procedure consists of dividing the sampling weights by estimates of the probabilities that the sampled units respond to the survey. Typically, these estimates are obtained by fitting parametric models, such as logistic regression. The resulting adjusted estimators may become biased when the specified parametric models are incorrect. To avoid misspecifying such a model, we consider nonparametric estimation of the response probabilities by local polynomial regression. We study the asymptotic properties of the resulting estimator under quasi-randomization. The practical behavior of the proposed nonresponse adjustment approach is evaluated on NHANES data.

    Release date: 2009-12-23
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