Adjusting for Selection Bias in Nonprobability Samples by Empirical Likelihood Approach

Pub Date : 2023-06-01 DOI:10.2478/jos-2023-0008
Daniela Marella
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Abstract

Abstract Large amount of data are today available, that are easier and faster to collect than survey data, bringing new challenges. One of them is the nonprobability nature of these big data that may not represent the target population properly and hence result in highly biased estimators. In this article two approaches for dealing with selection bias when the selection process is nonignorable are discussed. The first one, based on the empirical likelihood, does not require parametric specification of the population model but the probability of being in the nonprobability sample needed to be modeled. Auxiliary information known for the population or estimable from a probability sample can be incorporated as calibration constraints, thus enhancing the precision of the estimators. The second one is a mixed approach based on mass imputation and propensity score adjustment requiring that the big data membership is known throughout a probability sample. Finally, two simulation experiments and an application to income data are performed to evaluate the performance of the proposed estimators in terms of robustness and efficiency.
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用经验似然法调整非概率样本的选择偏差
摘要今天有大量的数据可用,比调查数据更容易、更快地收集,这带来了新的挑战。其中之一是这些大数据的不可能性,它们可能无法正确地代表目标人群,从而导致估计量存在高度偏差。本文讨论了在选择过程不可忽略的情况下处理选择偏差的两种方法。第一种基于经验似然性,不需要对总体模型进行参数化说明,而是需要对处于非概率样本中的概率进行建模。可以将总体已知的或可从概率样本估计的辅助信息作为校准约束,从而提高估计量的精度。第二种方法是基于大规模插补和倾向得分调整的混合方法,要求在整个概率样本中已知大数据隶属度。最后,进行了两个模拟实验并将其应用于收入数据,以评估所提出的估计器在稳健性和效率方面的性能。
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