Sampling and Intervention Designs

提出的研究为隐藏和难以接近的人群的抽样和干预设计开发了新的方法。这些种群包括用普通调查方法难以发现的动植物生态种群和人类种群。例如，动物和鱼类的种群可以是高度流动的，伪装的，并且随着时间的推移，在空间上的模式是不均匀的。***难以接近的人群包括那些隐藏的、***流动的和被污名化的人群。这些人群的成员可能只有通过社交网络链接才能联系到另一个成员。******提出的研究为具有空间、时间或网络结构的人群开发了新的网络、空间和时间抽样设计和干预策略。网络抽样对于调查隐藏的人群、理解组织动态以及理解在线网络和使用这些网络的人的行为都是必要的。对于分布不均匀的自然种群，需要自适应空间采样设计。在许多情况下，适应性和网络干预策略在减轻危害或为人类群体、组织和动植物群体带来利益方面比传统策略更有效。拟议的***研究继续了研究者的长期努力，为所有这些类型的***情况提供了有效的新方法。******在最近的工作中，我引入了简单的新估计器，用于对隐藏的和难以访问的人群进行网络采样。经验模拟表明，新的估计器比目前广泛使用的估计器有很大的改进。例如，对于估计隐藏种群中***个伴侣的平均数量，新方法几乎完全***消除了偏差，并将均方误差降低到目前*** *最广泛使用的估计器的*** 1 / 26到*** 1 / 80之间。所提出的研究将扩展新估计器的方法，并使高性能软件在开放访问下可用。******新网络采样估计器的关键技术是在样本网络数据上运行快速的网络采样过程。快速采样过程的包含频率为选择样本的现实世界网络采样设计提供了不相等包含概率的估计。然后将这些包含概率的估计用于广义不等概率估计器中来估计总体数量。置信区间由简化线性化方法得到。******拟议的研究将建立在这些新结果的基础上，寻找新的和改进的方法，也用于时空采样和推理，以及***进行干预，以使难以接近的、处于风险中的和服务不足的人群受益。*****

The proposed research develops new methods for sampling and***intervention designs for hidden and hard-to-access populations. These***include ecological populations of animals and plants and human***populations that are hard to find by ordinary survey methods.***Populations of animals and fish, for example, can be highly mobile,***camouflaged, and spatially uneven in patterns that change over time.***Hard-to-access human populations include those that are hidden,***mobile, and stigmatized. Members of such populations may be reachable***only by following social network links from one member to another.******The proposed research develops new network, spatial, and temporal***sampling designs and interventions strategies for populations that***have spatial, temporal, or network structure. Network sampling is***necessary for surveys of hidden human populations, for understanding***organizational dynamics as well as for understanding online networks***and the behaviours of people who use those networks. Adaptive spatial***sampling designs are needed for unevenly distributed natural***populations. In many cases also, adaptive and network intervention***strategies are more effective than conventional strategies for***mitigating harms or bringing benefits to human populations,***organizations, and populations of plants and animals. The proposed***research continues a long-term effort of the investigator that has***resulted in effective new methods for all of these types of***situations.******In recent work I introduced simple new estimators for network sampling***of hidden and hard-to-access human populations. Empirical simulations***show the new estimators greatly improved upon the estimators currently***in widespread use. For instance, for estimating mean number of***partners in the hidden population, the new methods almost totally***eliminated the bias and reduced mean sqare error to between***one-twenty-sixt to one-eightieth that of the currently most widely***used estimator. The proposed research will extend the methods of the***new estimators and make high-performance software available under open***access.******The key technique underlying the new network sampling estimators is to***run a fast network sampling process on the sample network data. The***inclusion frequencies of the fast sampling process provide estimates***of the unequal inclusion probabilities for the real-world network***sampling design by which the sample was selected. These estimates of***the inclusion probabilities are then used in generalized unequal***probability estimators to estimate population quantities. Confidence***intervals are obtained by a simplified linearization method.******The proposed research will build on these new results to find new and***improved methods also for spatial temporal sampling and inference and***for making interventions to benefit hard-to-access, at-risk, and***under-served populations.*****