Inference and computational methods for regression models in the presence of partially observed network data or high-dimensional capture-recapture data

存在部分观察到的网络数据或高维捕获-重捕获数据的回归模型的推理和计算方法

• 批准号: RGPIN-2022-03309
• 负责人: Yauck, Mamadou
• 金额: $ 1.38万
• 依托单位: Université du Québec à Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759343
• 关键词: Inference; computational; methods; regression; models

This Discovery research program is concerned with the development of statistical models for respondent-driven sampling (RDS) and capture-recapture experiments to tackle new theoretical and methodological challenges in data analysis. It puts the emphasis on the underlying theory and the computational aspects of new statistical models in the presence of partially observed network data or high-dimensional capture-recapture data. For RDS, we will first tackle the parameter estimation problem of regression models, under a new parameter identification paradigm, when the underlying population network is partially observed. We will propose novel semiparametric estimation methods under mild topological constraints on the structure of the network and under additional model assumptions. We will then relax the topological constraints and propose identification regions for the target parameters under partial identification. Once these principled approaches for RDS regression are developed, a framework for causal inference in RDS studies will be proposed. Further, two new design-based and model-based population size estimators will be developed by modeling the RDS process (i) as a wave-wise sampling design and by averaging over all possible reordering of the recruitment data and (ii) as a capture-recapture `removal' model in which individuals' recruitment probabilities depend on their network sizes, or degrees. In capture-recapture, new models and computational methods will be elaborated to analyze high-dimensional marketing data with an underlying network structure, generated by activating applications on mobile phones, with a focus on the estimation of direct and indirect effects of digital ads on foot traffic under a causal framework. We will adopt the potential outcome framework, with the additional assumption that an individual's exposition to an ad may affect her/his neighbors' decision to visit public places. The computational aspect of this research program will be further developed and implemented in open access software for a wider uptake by researchers and users. This Discovery research program will provide theoretical and methodological foundations for modern analysis of data collected via capture-recapture and RDS. This will offer a new understanding of these designs for data collection and analysis, which will be useful for applications in public health, social sciences, and new areas such as marketing and computational advertising.

该 Discovery 研究计划致力于开发受访者驱动抽样 (RDS) 和捕获-再捕获实验的统计模型，以应对数据分析中的新理论和方法挑战。它强调在存在部分观察到的网络数据或高维捕获-重捕获数据的情况下新统计模型的基础理论和计算方面。 对于 RDS，我们将首先在部分观察到底层群体网络时，在新的参数识别范式下解决回归模型的参数估计问题。我们将在网络结构的温和拓扑约束和附加模型假设下提出新颖的半参数估计方法。然后，我们将放宽拓扑约束，并提出部分识别下目标参数的识别区域。一旦开发出这些 RDS 回归的原则方法，就会提出 RDS 研究中因果推理的框架。此外，将通过对 RDS 过程进行建模（i）作为逐波抽样设计并对招募数据的所有可能重新排序进行平均，以及（ii）作为捕获来开发两个新的基于设计和基于模型的人口规模估计器-重新捕捉“移除”模型，其中个人的招募概率取决于其网络规模或程度。 在捕获-重新捕获中，将详细阐述新的模型和计算方法，以分析通过激活手机上的应用程序生成的具有底层网络结构的高维营销数据，重点是估计步行数字广告的直接和间接效果因果框架下的流量。我们将采用潜在结果框架，并附加假设，即个人对广告的接触可能会影响她/他的邻居访问公共场所的决定。该研究计划的计算方面将在开放获取软件中进一步开发和实施，以便研究人员和用户更广泛地采用。该发现研究计划将为通过捕获-再捕获和 RDS 收集的数据进行现代分析提供理论和方法基础。这将为数据收集和分析的这些设计提供新的理解，这对于公共卫生、社会科学以及营销和计算广告等新领域的应用非常有用。