Adaptive Sampling Designs in Network and Spatial Settings

网络和空间设置中的自适应采样设计

• 批准号: 0406229
• 负责人: James Rosenberger
• 金额: --
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2008-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0406229&HistoricalAwards=false
• 关键词: Adaptive; Sampling; Designs; Network; Spatial

The purpose of this research project is to develop new adaptivesampling designs and inference methods for sampling in network andspatially structured populations. Adaptive sampling designs are thosein which the procedure for selecting the sample can depend on valuesof variables of interest observed during the survey. In spatialsettings, that can mean adaptively adding new units to the sample inthe vicinity of high or otherwise interesting observed values. Innetwork or graph settings, links can be adaptively followed frominteresting sample nodes to add new nodes to the sample. A variety ofnew sampling procedures, together with design and model basedestimation methods, will be investigated in the study. A new,flexible and versatile class of adaptive designs, termed ``active setadaptive sampling,'' was found during the preliminary work toward thisproject. Designs in this class have certain advantages over adaptivecluster sampling and some of the traditional network sampling designsin being more flexible, allowing for control of total sample size andnot requiring complete inclusion of connected components.Design-unbiased estimates are possible with some of these designs,providing inferences that are robust against assumptions about thepopulation. These designs lend themselves toward model-basedinferences as well and can be used in some situations to help ensurethat the assumptions for the model-based inferences are met. Thisproject will advance the theory and methodology of adaptive samplingand in particular will fully investigate and develop severalcategories of new adaptive sampling designs within this class anddevelop and evaluate design and model based inference methods for usewith adaptive designs of all types.With adaptive sampling designs, the study design can change inresponse to the values and patterns observed during the study. Forexample, in a study of an at-risk hidden human population, sociallinks from particularly high-risk individuals can be followed to addmore individuals to the sample; in a survey of an unevenly distributednatural resource, new observations may be adaptively made inneighborhoods of high observed abundance. In previous work it hasbeen established that in many situations the theoretically optimalsampling strategy is an adaptive one. Specific adaptive designs, suchas the adaptive cluster sampling designs developed in a previousproject, have been shown to give substantial gains in precision orefficiency over conventional strategies for certain types ofpopulations, in particular rare, clustered ones. The results of theproposed research will provide research tools for other scientificfields, including the biological, environmental, health, and socialsciences. Each of these fields has to deal with populations that aredifficult to sample by conventional means because of theirunpredictably uneven spatial and network structures. The samplingmethods resulting from this project have applications to manysituations of importance to society, including studies of hiddenpopulations such as those at risk for HIV/AIDS, environmentalassessment and monitoring, biological surveys, natural resourcesexplorations and inventories, Internet surveys, rapid response tonatural and induced health threats, studies in human social behavior,and archaeological studies.

本研究的目的是发展新的适应性抽样设计和推理方法，用于网络和空间结构总体的抽样。 自适应抽样设计是指在抽样过程中，样本的选择取决于调查过程中观察到的变量值。 在空间设置中，这可能意味着在高或其他有趣的观察值附近自适应地向样本添加新的单位。 在网络或图形设置中，链接可以自适应地从感兴趣的样本节点向样本添加新节点。 该研究将研究各种新的抽样程序，以及基于设计和模型的估计方法。 一个新的，灵活的和通用的自适应设计类，称为“主动集自适应采样，”在对这个项目的初步工作中发现。 与自适应聚类抽样和一些传统的网络抽样设计相比，这类设计具有某些优势，因为它们更灵活，允许控制总样本大小，不需要完全包含连通分量。其中一些设计可以实现设计无偏估计，提供的推论对总体假设具有鲁棒性。 这些设计也适用于基于模型的推理，并且可以在某些情况下用于帮助确保基于模型的推理的假设得到满足。 本专题将推进适应性抽样的理论和方法，特别是将在本课程中全面研究和开发几类新的适应性抽样设计，并开发和评估用于所有类型适应性设计的设计和基于模型的推理方法。 例如，在一个处于危险中的隐藏人群的研究中，特别是高风险个体的社会联系可以被跟踪，以增加更多的个体到样本中;在一个不均匀分布的自然资源的调查中，新的观察可以在高观测丰度的社区中进行适应性的观察。 在以前的工作中，已经建立了在许多情况下，理论上最优采样策略是一个自适应的. 特定的适应性设计，如在以前的项目中开发的适应性整群抽样设计，已被证明在某些类型的群体，特别是稀有的，聚类的群体中，比传统的策略在精度或效率上有实质性的提高。 该研究成果将为生物、环境、健康和社会科学等其他科学领域提供研究工具。 这些领域中的每一个都必须处理难以通过传统手段进行采样的人群，因为他们不可预测的不均匀空间和网络结构。 该项目产生的抽样方法可应用于许多对社会有重要意义的情况，包括对隐藏人口的研究，如艾滋病毒/艾滋病的风险，环境评估和监测，生物调查，自然资源勘探和库存，互联网调查，对自然和诱发的健康威胁的快速反应，人类社会行为研究和考古研究。