Some theory for propensity-score-adjustment estimators in survey sampling

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2012-12-01
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Riddles, Minsun
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Kim, Jae Kwang
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Statistics
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The propensity-scoring-adjustment approach is commonly used to handle selection bias in survey sampling applications, including unit nonresponse and undercoverage. The propensity score is computed using auxiliary variables observed throughout the sample. We discuss some asymptotic properties of propensity-score-adjusted estimators and derive optimal estimators based on a regression model for the finite population. An optimal propensity-score-adjusted estimator can be implemented using an augmented propensity model. Variance estimation is discussed and the results from two simulation studies are presented.

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This article is published as Kim, J.K. and Riddles, M. (2012). “Some theory for propensity scoring adjustment estimator,” Survey Methodology 38, 157-165. Posted with permission.

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Sun Jan 01 00:00:00 UTC 2012
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