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要经过过滤、输入和其他形式的预处理。当采取这些步骤时，数据分析就成为数据收集、预处理和推理相关各方的协作努力。本研究将统计推断和学习的多方和多相范式定义为此类设置。这些设置充满了微妙和陷阱。每一方都没有，而且往往不可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一些判断的组合，并且一些预处理可能不可逆转地破坏原始数据中的信息。在他之前的理论和方法的基础上，PI和他的学生旨在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。他们的最终目标包括提供评估这种协作分析有效性的方法，统计原则预处理的指导，以及与多方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为构建更有用的科学数据库、处理复杂的测量过程和分析大量数据集提供原理和方法。随着用于科学发现、医学进步、教育改革和循证政策制定的数据的规模、多样性和复杂性的急剧增加，整个定量科学探究事业面临着前所未有的挑战和机遇。目前绝大多数的调查都不是由一个人或一个团队完成的。在实际应用中，科学数据的分析在很大程度上依赖于预处理。在数据收集之后，通常将原始数据转换为更易于处理的表单。这种转变从无害到极具破坏性。如果执行不当，它们可能会使巨额的科学投资在未来的分析中变得毫无用处。随着科学家和资助机构强调建设科学知识库和“大数据”，这些危险的重要性正在上升。尽管它很重要，但从理论的角度来看，人们对预处理的理解很少。即使在统计学家中，传统智慧和非正式指导也是常态。PI和他的学生将努力缩小这一差距，建立一个预处理和协作推理的理论。该理论旨在指导科学知识库的构建和对最新技术产生的海量数据集的分析。它还可以为加强合作和获取高质量科学数据打开大门，拓宽科学事业。

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.