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Multivariate Models and Methods for Correlated Data in Non-Standard Settings

Multivariate Models and Methods for Correlated Data in Non-Standard Settings
非标准设置中相关数据的多元模型和方法
批准号:
261821-2012
负责人:
deLeon, Alexander
金额:
$0.87万
依托单位:
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2012
资助国家:
加拿大
项目状态:
已结题
起止时间:
2012-01-01 至 2013-12-31

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中文摘要
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英文摘要
The advent of sophisticated tools of measurement has given rise to new modes of data collection resulting in very large complex data, oftentimes involving mixed discrete and continuous variables or high-dimensional binary data with complex multi-level correlations. Their complex structure typically requires non-standard statistical approaches that usually entail computationally intensive methodologies. These are particularly common in many applications in engineering, finance, and in medicine and health. Conventional tools that generally rely on the assumption that the data, or some suitable transformations of them, follow a Gaussian distribution, do not directly apply in these contexts.
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Models, methods and inference for non-standard correlated data
  • 批准号:
    RGPIN-2018-04748
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.33万
  • 财政年份:
    2022
  • 负责人:
    deLeon, Alexander
  • 依托单位:
Models, methods and inference for non-standard correlated data
  • 批准号:
    RGPIN-2018-04748
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.17万
  • 财政年份:
    2021
  • 负责人:
    deLeon, Alexander
  • 依托单位:
Models, methods and inference for non-standard correlated data
  • 批准号:
    RGPIN-2018-04748
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.17万
  • 财政年份:
    2020
  • 负责人:
    deLeon, Alexander
  • 依托单位:
Models, methods and inference for non-standard correlated data
  • 批准号:
    RGPIN-2018-04748
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.17万
  • 财政年份:
    2018
  • 负责人:
    deLeon, Alexander
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
新型手性NAD(P)H Models合成及生化模拟