论文标题
混合概率 - 偷球抽样
Hybrid Probabilistic-Snowball Sampling
论文作者
论文摘要
雪球抽样是人群中抽样设计的通用名称，要求受访者在社交关系中分享问卷。除某些例外，雪球采样的估计被认为是有偏见的。但是，偏见的大小受到采样设计和目标群体特征的组合的影响。混合概率 - 偷球采样设计（HPSSD）旨在通过随机过度放置雪球的第一阶段0，以减少雪球样本中的主要偏见来源。 为了检查HPSSD的应用程序的行为，我们开发了一种算法，该算法通过将随机块模型的边缘嫁接到一个集团图中，模拟了吸烟者的分类网络。也模拟了HPSSD操作的不同结果。 对8,000次仿真的推断会导致认为HPSSD不能提高已经代表性的样本的可靠性。但是，如果人口中的同质性足够低，那么即使是HPSSD的未调整的样本平均值也比随机但尺寸过小的采样的性能稍好一些。 降低HPSSD的估计值的估计值显示了性能的改善，因此调整后的HPSSD估计量是理想的发展。
Snowball sampling is the common name for sampling designs on human populations where respondents are requested to share the questionnaire among their social ties. With some exceptions, estimates from snowball samplings are considered biased. However, the magnitude of the bias is influenced by a combination of elements of the sampling design and features of the target population. Hybrid Probabilistic-Snowball Sampling Designs (HPSSD) aims to reduce the main source of bias in the snowball sample through randomly oversampling the first stage 0 of the snowball. To check the behaviour of HPSSD for applications, we developed an algorithm that, by grafting the edges of a stochastic blockmodel into a graph of cliques, simulates an assortative network of tobacco smokers. Different outcomes of the HPSSD operations are simulated, too. Inference on 8,000 runs of the simulation leads to think that HPSSD does not improve reliability of samples that are already representative. But if homophily in the population is sufficiently low, even the unadjusted sample mean of HPSSD has a slightly better performance than a random, but undersized, sampling. De-biasing the estimates of HPSSD shows improvement in the performance, so an adjusted HPSSD estimator is a desirable development.
