论文标题
一种减少自愿样品选择偏差的经验可能性方法
An empirical likelihood approach to reduce selection bias in voluntary samples
论文作者
论文摘要
我们在不可降值的样本选择模型下解决了自愿样品中的加权问题。在正确指定样品选择模型的假设下，我们可以计算模型参数的一致估计器，并构建人口均值的倾向得分估计器。我们使用经验可能性方法通过结合偏置校准约束和基准测定来构建自愿样本的最终权重。开发了该方法的线性化方差估计。还进行了有限的仿真研究，以检查所提出的方法的性能。
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.
