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MARTIN KLEIN - U.S. Census Bureau

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Imputation for Nonmonotone Nonresponse in the Survey of Industrial Research and Development
26 April 2012 from 4:00 PM to 5:00 PM
201 Thomas Bldg.
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Nonresponse in longitudinal studies often occurs in a nonmonotone pattern.  In the Survey of Industrial Research and Development (SIRD), it is reasonable to assume that the
nonresponse mechanism is past-value-dependent in the sense that the response propensity of a study variable at time point $t$ depends on response status and observed or missing values of the same variable at time points prior to $t$. Since this nonresponse is nonignorable, the parametric likelihood approach is sensitive to the specification of parametric models on both the joint distribution of variables at different time points and the nonresponse mechanism. The nonmonotone nonresponse also limits the application of inverse propensity weighting methods. By discarding all observed data from a subject after its first missing value, one can create a dataset with a monotone ignorable nonresponse and then apply established methods for ignorable nonresponse. However, discarding observed data is not desirable and it may result in inefficient estimators when many observed data are discarded. We propose to impute nonrespondents through regression under imputation models carefully created under the past-value-dependent nonresponse mechanism. This method does not require any parametric model on the joint distribution of the variables across time points or the nonresponse mechanism. Performance of the estimated means based on the proposed imputation method is investigated through some simulation studies and empirical analysis of the SIRD data.  This is joint work with Professor Jun Shao of the University of Wisconsin, Madison.

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