Statistical Methods for bias controlling in the analysis of rich data.

数字技术的创新和电子设备的使用产生了越来越多的丰富数据集，如大型行政数据库，以及通过实时移动的数据采集设备（手持计算机、智能手机、可穿戴设备等）收集的密集电子日记数据。尽管这些新的数据集很丰富，但由于各种常见的数据限制，它们的效用仍然有限，这些数据限制可能会给统计推断带来重大偏差。这些新型数据的规模和丰富性往往超过了传统方法控制偏差所需的计算资源。为了满足不同行业和部门对开发用于这些新类型数据的适当分析技术的强烈需求，该研究计划旨在开发一套新颖且有原则的偏差控制统计方法，这些方法也可扩展用于当今丰富的数据环境。 拟议的研究将建立在我的工作开发新的偏见控制方法，丰富的数据已发表在统计和定量科学期刊，并已在各种应用领域，如生命科学，生物医学工程，生物统计学，社会和健康科学，经济学和商业管理的影响。短期目标是开发新颖且易于处理的方法，以：A）量化因果推断对不可重复缺失的敏感性，B）量化聚类生存数据分析中对不可重复删失的敏感性，C）使用变量选择执行无分布多重插补以处理丰富数据应用中的缺失值，以及D）克服不可测量关键变量的问题。该研究计划的长期目标是开发新颖，通用，强大和计算可行的方法，以提高丰富数据的质量，可靠性，可用性和可访问性。 方法论方法将包括：i）简单模型和广义模型的分析推导，ii）通过计算机模拟实验研究性能，以及iii）应用于真实的数据集。 培训HQP和传播新的研究成果是拟议的研究计划的两个重要方面。该研究计划将为统计研究的各个方面提供跨学科培训，并将创新的统计方法应用于跨越许多行业和部门的独特数据集，包括政府机构，公司，非营利组织和学术机构。拟议的工作将激励和贡献新的大数据分析方法，并改善加拿大和国际上科学和工程领域的研究人员和从业人员如何分析和利用丰富的数据集。

Innovations in digital technology and use of electronic devices generate an increasing amount of rich data sets, such as large administrative databases, and intensive electronic diary data collected through real-time mobile data-capturing devices (handheld computers, smartphones, wearable devices etc.). Despite the richness of such new data sets, their utility remains limited due to various common data limitations that can introduce significant bias into statistical inference. The size and richness of these new kinds of data often outpace the computing resources needed for traditional approaches to controlling bias. In response to the strong demand from across different industries and sectors for developing appropriate analytic techniques for use with these new kinds of data, this research program aims to develop a set of novel and principled statistical methods for bias control that are also scalable for use in today's rich data environment. The proposed research will build on my work on developing new bias controlling methods for rich data that have been published in statistics and quantitative science journals, and have already had impact in various applied domains, such as life sciences, biomedical engineering, biostatistics, social and health sciences, economics and business management. The short-term objectives are to develop novel and tractable methods to: A) quantify the sensitivity of causal inference to nonignorable missingness, B) quantify the sensitivity to nonignorable censoring in the analysis of clustered survival data, C) perform distribution-free multiple imputation with variable selection to handle missing values in rich data applications, and D) overcome the issue of unmeasured key variables. The long-term goal of this proposed research program is to develop novel, general, robust and computationally feasible methodology to increase the quality, reliability, usability and accessibility of rich data. The methodological approach will include: i) analytical derivations for both simple and generalized models, ii) a study of performance through computer simulation experiments and iii) applications to real data sets. Training HQPs and disseminating new research results are two important aspects of the proposed research program. The research program will provide ample opportunities for interdisciplinary training in all aspects of statistical research and in developing and applying innovative statistical methods to unique data sets that span many industries and sectors, including government agencies, firms, nonprofit organizations and academic institutions. The proposed work will motivate and contribute new analytical methods for big data, and improve how researchers and practitioners in sciences and engineering in Canada and internationally can analyze and make use of rich data sets.