In recent years, non-probability samples, such as web survey samples, have become increasingly popular in many fields, but they may be subject to selection biases, which results in the difficulty for inference from them. Doubly robust (DR) estimation is one of the approaches to making inferences from non-probability samples. When many covariates are available, variable selection becomes important in DR estimation. In this paper, a new DR estimator for the finite population mean is constructed, where the intertwined probabilistic factors decoupling (IPAD) and modified IPAD are used to select important variables in the propensity score model and the outcome superpopulation model, respectively. Unlike the traditional variable selection approaches, such as adaptive least absolute shrinkage and selection operator and smoothly clipped absolute deviations, IPAD and the modified IPAD not only can select important variables and estimate parameters, but also can control the false discovery rate, which can produce more accurate population estimators. Asymptotic theories and variance estimation of the DR estimator with a modified IPAD are established. Results from simulation studies indicate that our proposed estimator performs well. We apply the proposed method to the analysis of the Pew Research Center data and the Behavioral Risk Factor Surveillance System data.

中文翻译：

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修正交织概率因素解耦的非概率样本双鲁棒估计

近年来，网络调查样本等非概率样本在许多领域越来越受欢迎，但它们可能存在选择偏差，导致难以从中进行推断。双鲁棒 (DR) 估计是从非概率样本中进行推断的方法之一。当有许多协变量可用时，变量选择在 DR 估计中变得很重要。在本文中，构建了一个新的有限总体均值 DR 估计器，其中交织概率因素解耦 (IPAD) 和改进的 IPAD 分别用于选择倾向评分模型和结果超总体模型中的重要变量。与传统的变量选择方法不同，IPAD 和改进的 IPAD 不仅可以选择重要变量和估计参数，还可以控制错误发现率，从而可以产生更准确的人口估计量。建立了改进 IPAD 的 DR 估计器的渐近理论和方差估计。模拟研究的结果表明我们提出的估计器表现良好。我们将所提出的方法应用于皮尤研究中心数据和行为风险因素监测系统数据的分析。建立了改进 IPAD 的 DR 估计器的渐近理论和方差估计。模拟研究的结果表明我们提出的估计器表现良好。我们将所提出的方法应用于皮尤研究中心数据和行为风险因素监测系统数据的分析。建立了改进 IPAD 的 DR 估计器的渐近理论和方差估计。模拟研究的结果表明我们提出的估计器表现良好。我们将所提出的方法应用于皮尤研究中心数据和行为风险因素监测系统数据的分析。