Matching a nonprobability sample to a probability sample is one strategy both for selecting the nonprobability units and for weighting them. This approach has been employed in the past to select subsamples of persons from a large panel of volunteers. One method of weighting, introduced here, is to assign a unit in the nonprobability sample the weight from its matched case in the probability sample. The properties of resulting estimators depend on whether the probability sample weights are inverses of selection probabilities or are calibrated. In addition, imperfect matching can cause estimates from the matched sample to be biased so that its weights need to be adjusted, especially when the size of the volunteer panel is small. Calibration weighting combined with matching is one approach to correct bias and reduce variances. We explore the theoretical properties of the matched and matched, calibrated estimators with respect to a quasirandomization distribution that is assumed to describe how units in the nonprobability sample are observed, a superpopulation model for analysis variables collected in the nonprobability sample, and the randomization distribution for the probability sample. Numerical studies using simulated and real data from the 2015 US Behavioral Risk Factor Surveillance Survey are conducted to examine the performance of the alternative estimators.

中文翻译：

---

调查非概率样本估计的备选方案：匹配加校准

将非概率样本与概率样本匹配是选择非概率单位和对它们加权的一种策略。过去曾采用这种方法从大量志愿者中选择人员子样本。此处介绍的一种加权方法是为非概率样本中的一个单元分配概率样本中其匹配个案的权重。结果估计量的属性取决于概率样本权重是选择概率的倒数还是经过校准。此外，不完全匹配会导致匹配样本的估计有偏差，因此需要调整其权重，尤其是当志愿者小组的规模较小时。校准加权与匹配相结合是纠正偏差和减少方差的一种方法。我们探索了匹配和匹配的校准估计量的理论特性，这些估计量与假设描述非概率样本中的单位如何被观察的准随机分布、非概率样本中收集的分析变量的超总体模型以及概率样本。使用 2015 年美国行为风险因素监测调查的模拟和真实数据进行数值研究，以检查替代估计量的性能。和概率样本的随机化分布。使用 2015 年美国行为风险因素监测调查的模拟和真实数据进行数值研究，以检查替代估计量的性能。和概率样本的随机化分布。使用 2015 年美国行为风险因素监测调查的模拟和真实数据进行数值研究，以检查替代估计量的性能。