Sampling and Intervention Designs

拟议的研究为隐藏和难以接触的人群开发了新的抽样和干预设计方法。 这些 * 包括动物和植物的生态种群和人类 * 种群，这些种群很难通过普通的调查方法找到。例如，动物和鱼类的种群可以是高度移动的，* 分散的，并且随着时间的推移而变化的模式在空间上是不均匀的。难以接触的人群包括那些隐藏的、* 移动的和被污名化的人群。 这些群体的成员可能只有通过从一个成员到另一个成员的社交网络链接才能达到 *。拟议的研究开发新的网络，空间和时间 * 抽样设计和干预策略的人口 * 有空间，时间或网络结构。 网络抽样对于调查隐藏的人口、了解 * 组织动态以及了解在线网络 * 和使用这些网络的人的行为是必要的。 适应性空间 * 抽样设计需要不均匀分布的自然 * 群体。 在许多情况下，适应性和网络干预 * 战略比传统战略更有效，可以 * 减轻危害或为人类、* 组织和动植物种群带来利益。 拟议中的研究继续了研究人员的长期努力，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，我介绍了简单的新估计网络抽样 * 隐藏和难以访问的人口。 经验模拟 * 表明，新的估计大大改善了目前 * 广泛使用的估计。 例如，对于估计隐藏群体中 * 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量的 * 26t到80t之间。 拟议的研究将扩展 * 新估计器的方法，并在开放 * 访问下提供高性能软件。*新的网络抽样估计器的关键技术是对样本网络数据进行快速网络抽样处理。 快速采样过程的 * 包含频率提供了对真实世界网络 * 采样设计（通过该设计选择样本）的不等包含概率的估计 *。 这些估计的包含概率，然后在广义不等 * 概率估计人口数量。 置信区间 * 通过简化的线性化方法获得。**拟议的研究将建立在这些新结果的基础上，以找到新的和 * 改进的方法，也用于时空采样和推断，并 * 进行干预，以使难以获得，有风险和 * 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.*****