Usage of non-statistical data sources for statistical purposes have become increasingly popular in recent years, also in official statistics. However, statistical inference based on non-probability samples is made more difficult by nature of them being biased and not representative of the target population. In this paper we propose quantile balancing inverse probability weighting estimator (QBIPW) for non-probability samples. We use the idea of Harms and Duchesne (2006) which allows to include quantile information in the estimation process so known totals and distribution for auxiliary variables are being reproduced. We discuss the estimation of the QBIPW probabilities and its variance. Our simulation study has demonstrated that the proposed estimators are robust against model mis-specification and, as a result, help to reduce bias and mean squared error. Finally, we applied the proposed method to estimate the share of vacancies aimed at Ukrainian workers in Poland using an integrated set of administrative and survey data about job vacancies

翻译：近年来，非统计性数据源在统计目的中的应用日益普及，在官方统计领域亦不例外。然而，基于非概率样本的统计推断因其固有的偏差性及对目标总体缺乏代表性而面临更大挑战。本文针对非概率样本提出分位数平衡逆概率加权估计量（QBIPW）。我们借鉴Harms与Duchesne（2006）的研究思路，将分位数信息纳入估计过程，从而复现辅助变量的已知总量与分布特征。本文系统探讨了QBIPW概率的估计方法及其方差估计问题。模拟研究表明，所提出的估计量对模型误设具有稳健性，能有效降低估计偏差与均方误差。最后，我们通过整合波兰职位空缺的行政记录与调查数据，应用该方法估算了面向乌克兰劳动者的职位空缺比例。