课题基金 / 基金详情

Computational Statistics

Computational Statistics
计算统计
批准号:
CRC-2015-00200
负责人:
McNicholas, Paul
金额:
$14.57万
依托单位:
依托单位国家:
加拿大
项目类别:
Canada Research Chairs
财政年份:
2017
资助国家:
加拿大
项目状态:
已结题
起止时间:
2017-01-01 至 2018-12-31

项目摘要

项目成果

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中文摘要
翻译
今天,越来越多的更大和更复杂的数据正在产生。超高维数据是最有问题的数据之一。统计学家和计算机科学家可以处理包含数千个变量的高维数据。然而,今天产生的数据可能有数万个变量,甚至更多。不幸的是,适用于高维数据的方法通常不适用于超高维数据。将开发超高维数据的计算统计方法,重点是找到同质子群或聚类的方法。这些方法将适用于从管理科学到疾病诊断和生物信息学的任何超高维数据出现的环境。
英文摘要
Today, increasingly larger and more complex data are being produced. Ultra high-dimensional data are among the most problematic. Statisticians and computer scientists can deal with high-dimensional data, which can contain thousands of variables. Data being produced today, however, can have tens of thousands of variables and even more. Unfortunately, methods that work for high-dimensional data are often unsuitable for ultra high-dimensional data. Computational statistics approaches for ultra high-dimensional data will be developed, focusing on methods that find homogeneous subgroups or clusters. These approaches will be applicable in any setting where ultra high-dimensional data arise, from management science to disease diagnostics and bioinformatics.
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Computational Statistics
  • 批准号:
    CRC-2021-00494
  • 项目类别:
    Canada Research Chairs
  • 资助金额:
    $7.29万
  • 财政年份:
    2022
  • 负责人:
    McNicholas, Paul
  • 依托单位:
Computational Statistics
  • 批准号:
    CRC-2015-00200
  • 项目类别:
    Canada Research Chairs
  • 资助金额:
    $7.29万
  • 财政年份:
    2022
  • 负责人:
    McNicholas, Paul
  • 依托单位:
Clustering and Classification Using Mixture Models: Towards Big Data
  • 批准号:
    RGPIN-2017-05255
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $6.27万
  • 财政年份:
    2021
  • 负责人:
    McNicholas, Paul
  • 依托单位:
Computational Statistics
  • 批准号:
    CRC-2015-00200
  • 项目类别:
    Canada Research Chairs
  • 资助金额:
    $14.57万
  • 财政年份:
    2021
  • 负责人:
    McNicholas, Paul
  • 依托单位:
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