Building a theoretical and methodological framework for collaborative statistical inference and learning: multi-party and multiphase paradigms

科学数据在分析之前几乎总是经过过滤、推算和其他形式的预处理。当采取这样的步骤时，数据分析就变成了参与数据收集、预处理和推理的各方的合作努力。这项研究将这样的设置称为统计推理和学习的多方和多阶段范式。这些设置充满了微妙之处和陷阱。每一方都不能也经常不能对手头的整个现象有一个完美的理解；最终的结果将不可避免地包含他们的判断的某种组合，并且一些预处理可能会不可逆转地破坏原始数据中的信息。在他以前的理论和方法的基础上，PI和他的学生们致力于发展一套统计理论和方法来理解这些问题，并提供更好的预处理、推理和学习。它们的最终目标包括提供评估这种协作分析有效性的方法，指导统计原则的预处理，以及与多方进行统计学习和推理的丰富的新理论。他们开发的理论框架可以阐明构建更有用的科学数据库、处理复杂的测量过程和分析海量数据的原则和方法。随着可用于科学发现、医学进步、教育改革和循证政策制定的数据的大小、多样性和复杂性的急剧增加，整个定量科学研究事业面临着前所未有的挑战和机遇。目前的绝大多数调查都不是由一个人，甚至不是一个团队进行的。特别是，科学数据的分析在很大程度上依赖于实践中的预处理。在数据收集之后，原始数据通常被转换为更容易处理的形式。这样的转变从无害到极具破坏性不等。如果执行不力，它们可能会摧毁巨大的科学投资，使其在未来的分析中毫无用处。随着科学家和资助机构强调科学储存库和“大数据”的建设，这些危险的重要性正在上升。尽管预处理很重要，但从理论角度来看，人们对它的理解很少。即使在统计学家中，传统的智慧和非正式的指导也是常态。PI和他的学生将努力缩小这一差距，建立一种预处理和协作推理的理论。这一理论旨在指导科学知识库的建设和对最新技术产生的海量数据集的分析。它还可以为更好的合作和获得高质量的科学数据打开大门，扩大科学事业。

Scientific data almost always undergo filtering, imputation, and other forms of preprocessing before they are analyzed. When such steps are taken, the data analysis becomes a collaborative endeavor by all parties involved in data collection, preprocessing, and inference. This research terms such settings as falling within the multiparty and multiphase paradigms for statistical inference and learning. These settings are rife with subtleties and pitfalls. Each party does not and often cannot have a perfect understanding of the entire phenomenon at hand; the final results will inevitably contain some combination of their judgments, and some preprocessing can irreversibly destroy information from the raw data. Building upon his previous theory and methods for dealing with uncongeniality with multiple imputation, the PI and his students aim to develop a set of statistical theory and methods to understand such problems and to provide better preprocessing, inferences, and learning. Their ultimate goals include providing methods for assessing the validity of such collaborative analyses, guidance on statistically-principled preprocessing, and a rich new theory of statistical learning and inference with multiple parties. The theoretical framework they develop can shed light on principles and methods for constructing more useful scientific databases, handling complex measurement processes, and analyzing massive datasets.With the dramatic increases in the size, diversity, and complexity of data available for scientific discoveries, medical advances, education reforms and evidence-based policy making, the entire enterprise of quantitative scientific inquiry has been presented with unprecedented challenges and opportunities. The vast majority of current inquiries are not made by a single individual or even a single team. In particular, the analysis of scientific data depends heavily on preprocessing in practice. Following data collection, raw data is typically transformed into a more easily handled form. Such transformations range from innocuous to highly destructive. When poorly executed, they can destroy huge scientific investments by rendering them useless for future analyses. These dangers are rising in importance as scientists and funding agencies emphasize the construction of scientific repositories and "big data". Despite its importance, preprocessing is poorly understood from a theoretical perspective. Even among statisticians, conventional wisdom and informal guidance are the norm. The PI and his students will work to close this gap, building a theory of preprocessing and collaborative inference. This theory aims to guide the construction of scientific repositories and the analysis of massive datasets generated by the latest technologies. It can also open the doors to greater collaboration and access to high-quality scientific data, broadening the scientific enterprise.