An empirical likelihood approach to reduce selection bias in voluntary samples

Abstract

We address the weighting problem in voluntary samples under a nonignorable sample selection model. Under the assumption that the sample selection model is correctly specified, we can compute a consistent estimator of the model parameter and construct the propensity score estimator of the population mean. We use the empirical likelihood method to construct the final weights for voluntary samples by incorporating the bias calibration constraints and the benchmarking constraints. Linearization variance estimation of the proposed method is developed. A limited simulation study is also performed to check the performance of the proposed methods.