A Spectral Framework for Network-Driven Sampling

网络驱动采样的频谱框架

• 批准号: 1612456
• 负责人: Karl Rohe
• 金额: $ 17.24万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1612456&HistoricalAwards=false
• 关键词: Spectral; Framework; Network; Driven; Sampling

Probability sampling drastically reduces the burden of research in various disciplines because statistical inference can extend conclusions from a sample to the entire population. However, classical sampling techniques require a sampling frame that lists each individual in the population and a way of contacting each individual. In many settings, a sampling frame is not available. In others, a sampling frame is too expensive to compile or only covers a biased subset of the population. Particularly with hard-to-reach populations, network-driven sampling provides one of the only ways to find members of the population. Leveraging a network to find a target population appears in many disciplines with a multitude of names: respondent-driven sampling, snowball sampling, web crawling, link-tracing, breadth-first search, co-immunoprecipitation, and chromatin immunoprecipitation. These disparate techniques all provide access to hard-to-reach and networked populations by essentially asking participants to refer friends. As a result, these are all network-driven techniques. Classical sampling theory does not apply to network-driven sampling because friends are similar; this induces dependence between samples that is influenced by the underlying social network. Preliminary research conducted by the investigator identifies a critical threshold that relates the structure of the social network to the referral rate in the sampling tree; beyond this critical threshold, standard network-driven approaches produce highly uncertain estimates. This research aims to produce new statistical techniques that continue to perform well beyond the critical threshold. Moreover, this project will study novel forms of network-driven data collection that incorporate additional information to produce more representative samples. Classical sampling results are not applicable to network-driven sampling because friends are similar, inducing dependence between samples. Previous theoretical results show that some network-driven studies do not obtain square root n-consistent estimators. Whether a study obtains square root n-consistency depends on both (i) the spectral properties of the underlying social network and (ii) the growth of the sampling tree. This research aims to provide new estimators that correct for the dependence between samples. These dependence-corrected estimators can obtain square root n-consistency, even when current estimators do not. This project will also construct new diagnostics and new sampling designs for network-driven sampling. The new spectral framework will provide a suite of theory, methodology, and practices that will enable studies to obtain square root n-consistent estimators.

概率抽样极大地减轻了各个学科的研究负担，因为统计推断可以将结论从样本扩展到整个人群。 然而，传统的抽样技术需要一个抽样框架，列出人口中的每一个人和接触每一个人的方式。在许多设置中，采样帧不可用。 在另一些情况下，抽样框架过于昂贵，无法汇编，或仅涵盖人口中有偏见的一部分。 特别是对于难以接触的人群，网络驱动的抽样提供了找到人群成员的唯一方法之一。利用网络来寻找目标人群出现在许多学科中，有许多名称：响应者驱动的采样，滚雪球采样，网络爬行，链接跟踪，广度优先搜索，免疫共沉淀和染色质免疫沉淀。 这些完全不同的技术都通过要求参与者推荐朋友来提供对难以接触和网络化人群的访问。因此，这些都是网络驱动的技术。经典的抽样理论不适用于网络驱动的抽样，因为朋友是相似的;这会导致样本之间的依赖性，受潜在的社会网络的影响。 调查员进行的初步研究确定了一个关键阈值，该阈值将社交网络的结构与抽样树中的转介率联系起来;超过这个关键阈值，标准的网络驱动方法会产生高度不确定的估计。这项研究的目的是产生新的统计技术，继续执行远远超过临界阈值。此外，该项目还将研究网络驱动的数据收集的新形式，其中包括更多的信息，以产生更具代表性的样本。经典的抽样结果不适用于网络驱动的抽样，因为朋友是相似的，诱导样本之间的依赖性。 以往的理论结果表明，一些网络驱动的研究没有得到平方根n-相容估计。研究是否获得平方根n-一致性取决于（i）底层社交网络的频谱特性和（ii）采样树的增长。本研究旨在提供新的估计，纠正样本之间的依赖性。这些依赖校正估计可以获得平方根n-一致性，即使当前的估计不。该项目还将为网络驱动的采样构建新的诊断和新的采样设计。新的谱框架将提供一套理论，方法和实践，使研究获得平方根n-一致估计。