标题： 概率样本与非概率样本的混合：对军人照护者调查的应用 显示英文标题

标题： Blending of Probability and Non-Probability Samples: Applications to a Survey of Military Caregivers

摘要： 概率样本是提供可推广到更大总体的推断的首选方法。 然而，当一个小（或罕见）的子总体是感兴趣群体时，这种方法可能无法产生足够大的样本量以产生精确的推断。 非概率（或便利）抽样通常能提供必要的样本量以获得有效的估计，但选择偏差可能会损害结果在更广泛总体中的可推广性。 激发阐述的是对军人照护者的调查；我们的兴趣集中在无报酬的照护者身上，这些照护者照顾的是在2001年9月11日后在美国武装部队服役的伤残、生病或受伤的现役人员和退伍军人。 一项广泛的概率抽样努力仅从这个子总体中获得了72名照护者。 因此，我们考虑用同一子总体的便利样本来补充概率样本，并开发了新的统计加权方法，这些方法可用于结合（或混合）样本。 我们的分析表明，感兴趣的子总体所承受的困难比较早服役的退伍军人的照护者更大，当使用具有所提议加权方案的混合样本时，这些结论更为明显。 我们通过模拟研究得出结论，这些研究说明了所提出技术的有效性，检验了在使用未充分混合的数据时遇到的偏差-方差权衡，并表明在结果与用于混合的辅助变量密切相关的情况下，便利样本提供的精度增益较低。 显示英文摘要

摘要： Probability samples are the preferred method for providing inferences that are generalizable to a larger population. However, when a small (or rare) subpopulation is the group of interest, this approach is unlikely to yield a sample size large enough to produce precise inferences. Non-probability (or convenience) sampling often provides the necessary sample size to yield efficient estimates, but selection bias may compromise the generalizability of results to the broader population. Motivating the exposition is a survey of military caregivers; our interest is focused on unpaid caregivers of wounded, ill, or injured servicemembers and veterans who served in the US armed forces following September 11, 2001. An extensive probability sampling effort yielded only 72 caregivers from this subpopulation. Therefore, we consider supplementing the probability sample with a convenience sample from the same subpopulation, and we develop novel methods of statistical weighting that may be used to combine (or blend) the samples. Our analyses show that the subpopulation of interest endures greater hardships than caregivers of veterans with earlier dates of service, and these conclusions are discernably stronger when blended samples with the proposed weighting schemes are used. We conclude with simulation studies that illustrate the efficacy of the proposed techniques, examine the bias-variance trade-off encountered when using inadequately blended data, and show that the gain in precision provided by the convenience sample is lower in circumstances where the outcome is strongly related to the auxiliary variables used for blending.