New Tools for Sparse Inference in Large-scale Multiple Comparisons

主要研究者： Jin，Jiashun提案编号：DMS -0505423提案标题：大规模多重比较中稀疏推理的新工具 研究机构：普渡大学 提出了一项研究工作，以创建新的工具，大规模的多重比较。 在这一领域的工作一直集中在理想化的模型，如标准高斯模型，什么还没有得到解决的是许多其他模型，具有更现实的和影响的潜力。在这一建议中，调查员研究了三个方面的问题：（a）。大量数据的公式化-开发具有科学现实性和影响力的模型，以及数学简单性，以便仔细研究。（B）。 开发新工具-通过探索各种各样的模式，揭示新现象，开发易于实施和理论上合理的工具。（c）.精细渐近研究--为渐近研究提供框架，仔细比较现有和新提出的推理工具，研究此类工具的最优性。本项目的动机在于，在基因组学、天文学和图像处理等科学领域产生的大量数据集导致了统计学的一个新领域：大规模同时假设检验或多重比较。这个新领域的进步将使来自各个科学领域的科学家能够从大量数据集中快速提取他们所需的信息，开发易于实施的工具是统计界的直接利益。该项目通过开发新工具和新理论以及揭示新现象来推动该领域的边界。该项目产生的工具，在理论上健全和实际可行的解决问题的领域，如基因组学，天文学和图像处理。

ABSTRACTPrincipal Investigators: Jin, Jiashun Proposal Number: DMS - 0505423Proposal Title: New Tools for Sparse Inference in Large-scale Multiple Comparisons Institution: Purdue University A research effort is proposed to create new tools for large-scale multiple comparisons. Work in this field has been concentrated on idealized models such as the standard Gaussian model, what has not been addressed is the potential of many other models which have more realism and impact. In this proposal, the investigator studies problems in three areas: (a). Formulation of massive data -- Develop models which have Scientific realism and impact, as well as mathematical simplicity such that careful study is possible. (b). Development of new tools -- By exploring a wide variety of models, expose new phenomena and develop tools which are easy-to-implement and theoretically sound. (c). Delicate asymptotic study -- Lay out framework for asymptotic study, carefully compare the existing and newly proposed inference tools, study on the optimality of such tools.The motivation of this project lies in that, massive datasets produced in scientific areas such as Genomics, astronomy, and image processing lead to a new field in statistics: large-scale simultaneous hypothesis testing or multiple comparisons. The vision is advances in this new field will enable the scientists from various scientific fields to quickly extract the information they need from massive datasets, and it is the immediate interest of the statistics community to develop easy-to-implement tools. This project pushes the boundary of the field by developing new tools and novel theories, as well as exposing new phenomena. The project produces tools which are theoretically sounding and practically feasible for solving problems in areas such as Genomics, astronomy, and image processing.