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RUI: Multivariate Calibration as a Harmonious and Parsimonious Problem

RUI: Multivariate Calibration as a Harmonious and Parsimonious Problem
RUI:多元校准是一个和谐且简约的问题
批准号:
0400034
负责人:
John Kalivas
金额:
$11.88万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2004
资助国家:
美国
项目状态:
已结题
起止时间:
2004-08-01 至 2007-07-31

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中文摘要
翻译
爱达荷州立大学的约翰·卡利瓦斯教授接受了一项统计研究的资助,该研究旨在改进定量结构活性关系(QSAR)计算和光谱数据分析等。这些活动属于被称为“化学计量学”的专门领域。我们的想法是开发多变量校准,当预测值的数量超过样本数量(pn,而不是通常的假设np)时,这是有意义的。PI使用回归向量套索(最小绝对收缩和选择操作符)和其他规范(带约束)来拟合此类数据。正在与岭回归、偏最小二乘和多元线性回归等其他方法进行比较。这项工作是由本科生使用MatLab程序完成的。与匈牙利科学院中央化学研究所的Karoly Heberger博士的合作将扩大QSAR研究。该奖项由化学部的分析和表面化学计划以及数学部的统计计划共同资助,该计划属于NSF范围的数学科学优先领域。数据分析的效率和有效性在包括生物学在内的所有量化科学中正变得越来越重要。为了在这些领域取得进展,需要将最先进的统计方法整合到自然科学学科中。对本科生进行这些先进方法的教育,为他们从事任何类型的科学专业工作做好准备。
英文摘要
Professor John Kalivas of Idaho State University is funded for a statistical study aimed at improving, for example, quantitative structure activity relationships (QSAR) calculations, and analysis of spectroscopic data. These activities fall in the specialized field called "chemometrics." The idea is to develop multivariate calibration, which is of interest when the number of predictors exceeds the number of samples (pn, rather than the usual assumption np). The PI is using the regression vector LASSO (least absolute shrinkage and selection operator) and other norms (with constraints) to fit such data. Comparisons to other methods such as ridge regression, partial least squares and multiple linear regression, are being performed. The work is being done by undergraduate students using the program MATLAB. Collaboration with Dr. Karoly Heberger of the Central Research Institute for Chemistry of the Hungarian Academy of Sciences will extend the QSAR studies.This award is co-funded by the Analytical and Surface Chemistry program of the Chemistry Division and the Statistics program of the Division of Mathematical Sciences under the umbrella of the NSF-wide Mathematical Sciences Priority Area. Efficiency and effectiveness of data analysis is becoming increasingly important in all of the quantitative sciences, including biology. The integration of state of the art statistical methods within the natural science disciplines is required for advancement in these fields. Education of undergraduates in these advanced methods prepare them for any type of scientific professional pursuit.
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CDS&E: Immersive Virtual Reality for Discovering Hidden Chemical Information and Improving Multivariate Modeling and Predication
  • 批准号:
    2305020
  • 项目类别:
    Standard Grant
  • 资助金额:
    $45.0万
  • 财政年份:
    2023
  • 负责人:
    John Kalivas
  • 依托单位:
CDS&E: Adaptive Learning for Multivariate Calibration with Big Data Attributes
  • 批准号:
    1904166
  • 项目类别:
    Standard Grant
  • 资助金额:
    $40.0万
  • 财政年份:
    2019
  • 负责人:
    John Kalivas
  • 依托单位:
CDS&E: Regularization Adaption Processes for Multivariate Calibration and Maintenance
  • 批准号:
    1506417
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $45.33万
  • 财政年份:
    2015
  • 负责人:
    John Kalivas
  • 依托单位:
RUI: Dynamic Net Analyte Signal Modeling for Multivariate Calibration and Maintenance
  • 批准号:
    1111053
  • 项目类别:
    Standard Grant
  • 资助金额:
    $30.11万
  • 财政年份:
    2011
  • 负责人:
    John Kalivas
  • 依托单位:
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