Journal or Book Title
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.
Source: Statistics Canada; Survey Methodology; December 2012. Reproduced and distributed on an "as is" basis with the permission of Statistics Canada.
Minister of Industry
Kim, Jae Kwang and Riddles, Minsun Kim, "Some theory for propensity-score-adjustment estimators in survey sampling" (2012). Statistics Publications. 107.