Collaborative Research: A Paradigm for Dimension Reduction with Respect to a General Functional

该研究的目的是开发一个通用的配方和相关的方法，充分降维（SDR）的条件分布的特定功能（或参数）是感兴趣的。特别提款权方法在过去的二十年里得到了蓬勃发展，并取得了令人瞩目的成功应用。然而，在很大程度上，这些方法将条件分布视为感兴趣的对象，而不区分感兴趣的参数和讨厌的参数。虽然有针对统计泛函的方法，但它们特定于所考虑的参数，因此难以应用于其他参数。研究人员提出了一种新的SDR范式，专注于条件分布的泛函，它可以是涵盖大多数应用的非常广泛的类别中的任何一个。此外，研究人员建议开发一个连贯的收集相关技术的估计，计算和渐近推理。产生大量复杂和高维数据的高吞吐量技术在商业、政府管理、环境研究、机器学习和生物信息学等不同领域越来越普遍。这些为统计界提供了相当大的动力，以开发新的理论和方法，并重新制定现有的理论和方法，这些理论和方法能够从高维和海量数据中发现关键证据。特别提款权是在这些新需求中产生并受到这些新需求推动的一个最新统计研究领域。研究人员建议重新制定特别提款权的理论和方法，使它们能够专门针对要估计的目标。这种新的范式不仅综合、拓宽和深化了SDR的最新进展，而且通过遵循充分性、有效性、信息、感兴趣的参数和讨厌的参数的传统，使SDR的理解与经典的统计推断理论相提并论，这些传统是推动经典推断走向成熟的关键思想

The proposed research aims to developing a general formulation and the related methods for sufficient dimension reduction (SDR) where a specific functional (or parameter) of the conditional distribution is of interest. The past two decades have seen vigorous development of the SDR methods and have accrued a striking record of their successful applications. However, to a large extent these methods treat the conditional distribution as the object of interest, without discriminating between parameter of interest and nuisance parameter. While there are methods that target statistical functionals, they are specific to the parameter in consideration and as such are difficult to apply to other parameters. The investigators propose a new paradigm for SDR that focuses on a functional of the conditional distribution, which can be any one in a very wide class that covers most of applications. In addition, the investigators propose to develop a coherent collection of associated techniques for estimation, computation, and asymptotic inference. High throughput technologies that produce massive amount of complex and high-dimensional data are increasingly prevalent in such diverse areas as business, government administration, environmental studies, machine learning, and bioinformatics. These provide considerable momentum in the Statistics community to develop new theories and methodologies, and to reformulate the existing ones, that are capable of discovering critical evidence from high-dimensional and massive data. SDR is a recent area of statistical research that arose amidst, and has been propelled by, these new demands. The investigators propose to reformulate the theories and methodologies of SDR so that they can be specifically tailored to target to be estimated. This new paradigm not only synthesizes, broadens, and deepens the recent advances in SDR, but brings the understanding of SDR on a par with classical statistical inference theory, by following the tradition of sufficiency, efficiency, information, parameter of interests, and nuisance parameters, which are the key ideas that has helped to propel classical inference to its maturity