New Tools for Sparse Inference in Large-scale Multiple Comparisons

大规模多重比较中稀疏推理的新工具

基本信息

  • 批准号:
    0505423
  • 负责人:
  • 金额:
    $ 9万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2005
  • 资助国家:
    美国
  • 起止时间:
    2005-07-15 至 2008-06-30
  • 项目状态:
    已结题

项目摘要

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.
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。

项目成果

期刊论文数量(0)
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会议论文数量(0)
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Jiashun Jin其他文献

SCORE+ for Network Community Detection
网络社区检测 SCORE
  • DOI:
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jiashun Jin;Z. Ke;Shengming Luo
  • 通讯作者:
    Shengming Luo
Supplement of ``Estimating Network Memberships by Simplex Vertex Hunting"
《通过单纯形顶点狩猎估计网络成员资格》的补充
  • DOI:
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jiashun Jin;Z. Ke;Shengming Luo
  • 通讯作者:
    Shengming Luo
MEDLINE/ PubMed
MEDLINE/PubMed
  • DOI:
    10.1007/978-0-387-39940-9_3039
  • 发表时间:
    2004
  • 期刊:
  • 影响因子:
    3.8
  • 作者:
    Cornelia Caragea;V. Honavar;P. Boncz;P. Larson;S. Dietrich;Gonzalo Navarro;Bhavani Thuraisingham;Yan Luo;Ouri E. Wolfson;S. Beitzel;Eric C. Jensen;Ophir Frieder;Christian S. Jensen;N. Tradisauskas;Ethan V. Munson;A. Wun;K. Goda;Stephen E. Fienberg;Jiashun Jin;Guimei Liu;Nick Craswell;T. Pedersen;Cesare Pautasso;M. Moro;S. Manegold;B. Carminati;Marina Blanton;Sara Bouchenak;Noël de Palma;Wei Tang;Christoph Quix;M. Jeusfeld;R. K. Pon;David J. Buttler;W. Meng;P. Zezula;Michal Batko;Vlastislav Dohnal;J. Domingo;Denilson Barbosa;Ioana Manolescu;Jeffrey Xu Yu;Emmanuel Cecchet;Vivien Quéma;Xifeng Yan;G. Santucci;D. Zeinalipour;Panos K. Chrysanthis;Amol Deshpande;Carlos Guestrin;Samuel Madden;Carson Kai;R. H. Güting;Amarnath Gupta;Heng Tao Shen;G. Weikum;Ramesh Jain;Jeffrey Xu Yu;Paolo Ciaccia;K. Candan;M. Sapino;C. Meghini;F. Sebastiani;U. Straccia;F. Nack;V. S. Subrahmanian;Maria Vanina Martinez;D. Reforgiato;T. Westerveld;M. Sebillo;G. Vitiello;Maria De Marsico;K. Voruganti;C. Parent;S. Spaccapietra;Christelle Vangenot;Esteban Zimányi;Prasan Roy;S. Sudarshan;E. Puppo;Peer Kröger;Matthias Renz;H. Schuldt;Solmaz Kolahi;A. Unwin;W. Cellary
  • 通讯作者:
    W. Cellary
Estimation and Confidence Sets for Sparse Normal Mixtures
稀疏正态混合物的估计和置信集
  • DOI:
    10.1214/009053607000000334
  • 发表时间:
    2006
  • 期刊:
  • 影响因子:
    4.5
  • 作者:
    T. Cai;Jiashun Jin;Mark G. Low
  • 通讯作者:
    Mark G. Low
Privacy-Preserving Data Sharing in High Dimensional Regression and Classification Settings
高维回归和分类设置中的隐私保护数据共享
  • DOI:
    10.29012/jpc.v4i1.618
  • 发表时间:
    2012
  • 期刊:
  • 影响因子:
    0
  • 作者:
    S. Fienberg;Jiashun Jin
  • 通讯作者:
    Jiashun Jin

Jiashun Jin的其他文献

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{{ truncateString('Jiashun Jin', 18)}}的其他基金

Feature selection in several challenging directions
几个具有挑战性的方向的特征选择
  • 批准号:
    2310668
  • 财政年份:
    2023
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant
New Tools for Analyzing Complex Network and Text Data
用于分析复杂网络和文本数据的新工具
  • 批准号:
    2015469
  • 财政年份:
    2020
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant
New Tools for Large-Scale Sparse Inference
用于大规模稀疏推理的新工具
  • 批准号:
    1513414
  • 财政年份:
    2015
  • 资助金额:
    $ 9万
  • 项目类别:
    Continuing Grant
Rare and Weak Signals in Big Data: How to Find Them and How to Use Them
大数据中的稀有信号和微弱信号:如何找到它们以及如何使用它们
  • 批准号:
    1208315
  • 财政年份:
    2012
  • 资助金额:
    $ 9万
  • 项目类别:
    Standard Grant
CAREER: Inferences on Large-Scale Multiple Comparisons: The Temptation of the Fourier Kingdom
职业:大规模多重比较的推论:傅里叶王国的诱惑
  • 批准号:
    0908613
  • 财政年份:
    2008
  • 资助金额:
    $ 9万
  • 项目类别:
    Continuing Grant
CAREER: Inferences on Large-Scale Multiple Comparisons: The Temptation of the Fourier Kingdom
职业:大规模多重比较的推论:傅里叶王国的诱惑
  • 批准号:
    0639980
  • 财政年份:
    2007
  • 资助金额:
    $ 9万
  • 项目类别:
    Continuing Grant

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