Abstract #300721

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JSM 2003 Abstract #300721
Activity Number: 443
Type: Contributed
Date/Time: Thursday, August 7, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Survey Research Methods
Abstract - #300721
Title: A Kernel Smoothing Method to Adjust for Unit Nonresponse in Sample Surveys
Author(s): Damiao N. Da Silva*+ and Jean D. Opsomer
Companies: Iowa State University and Iowa State University
Address: 57 Schilletter Vlg. Apt. D, Ames, IA, 50010-8757,
Keywords: missing data ; weighting adjustments ; response probability ; local polynomial regression
Abstract:

Unit nonresponse is a common problem in survey sampling that, if left unaccounted for, can invalidate inferences from the survey results. This type of nonresponse is usually handled by weighting methods, which are based on adjusting the sampling weights of the respondents to compensate for the nonrespondents. Such weighting adjustments inevitably require a model for the unobserved nonresponse mechanism, as for instance done in the frequently used "response homogeneity group" (weighting cell) estimators. However, if this nonresponse model is misspecified, it can itself introduce bias in the survey estimators. To try to minimize this misspecification problem, we model the nonresponse mechanism as a smooth but otherwise completely unspecified function of an observed covariate. We use kernel regression to estimate the unknown response probabilities and then use the inverses of these estimated probabilities as a weighting adjustment. We discuss the theoretical and practical properties of this estimator.

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