Sampling and Intervention Designs

拟议的研究开发了新的采样方法， 为隐蔽和难以接触的人群设计干预措施。 这些 包括动物、植物和人类生态种群 用普通调查方法很难找到的人群。 例如，动物和鱼类的种群可以是高度移动的， 在空间上不均匀的模式，随着时间的推移而变化。 难以接近的人群包括那些隐藏的人， 移动的，并且被污名化。 这些群体的成员可能是可达的 只能通过社交网络从一个成员到另一个成员的链接。 拟议的研究开发新的网络，空间和时间 人口抽样设计和干预战略， 具有空间、时间或网络结构。 网络采样是 调查隐藏的人口，了解 组织动态以及理解在线网络 以及使用这些网络的人的行为。 自适应空间 对于不均匀分布的自然资源， 人口。 在许多情况下，适应性和网络干预 战略比传统战略更有效， 减轻危害或造福人类， 组织和动植物种群。 拟议 研究继续了研究人员的长期努力， 为所有这些类型的人带来了有效的新方法， situations. 在最近的工作中，我介绍了简单的网络抽样新估计 隐藏的难以接近的人群 经验模拟 显示新的估计大大改善了目前的估计 广泛使用。 例如，为了估计 在隐藏的人口的合作伙伴，新的方法几乎完全 消除了偏差，并将均方误差降低到 是目前最广泛使用的 使用估计器。 拟议的研究将扩展的方法， 新的估计器，并使高性能软件在开放的 access. 新网络抽样估计器的关键技术是 对所述样本网络数据运行快速网络采样过程。 的 快速采样过程的包含频率提供了估计 真实网络的不相等包含概率 抽样设计是用来选择样本的。 这些估计 包含概率然后被用于广义不相等 估计人口数量的概率估计器。 信心 通过简化的线性化方法获得间隔。 拟议的研究将建立在这些新结果的基础上， 还用于空间时间采样和推断的改进方法， 进行干预，使难以获得的，有风险的， 服务不足的人群。

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.